LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework

# LaTA: A Drop-in, FERPA-Compliant Local-LLM Autograder for Upper-Division STEM Coursework
Source: [https://arxiv.org/html/2605.05410](https://arxiv.org/html/2605.05410)
Jesse A\. Rodríguez School of Mechanical, Industrial, and Manufacturing Engineering Oregon State University Corvallis, OR 97331, USA jesse\.rodriguez@oregonstate\.edu

###### Abstract

Large\-language\-model \(LLM\) graders promise to relieve the grading burden of upper\-division STEM courses, but most deployments to date send student work to third\-party APIs, violating FERPA and exposing institutions to data risk while requiring substantial assignment modification\. We presentLaTA\(LaTeX Teaching Assistant\), a drop\-in, open\-source autograder that runs entirely on commodity on\-premises hardware and assumes a LaTeX\-native workflow already adopted by many engineering and physics courses\. LaTA implements a four\-stage pipeline \(ingest, segment, grade, report\) using a locally hosted open\-weight chain\-of\-thought LLM grader \(gpt\-oss:120b\) that compares student work to an instructor\-authored reference solution and applies a YAML rubric with binary per\-item scoring\. We deployed LaTA in Winter 2026 in ME 373 \(Mechanical Engineering Methods\) at Oregon State University, grading every weekly assignment for approximately 200 students on a single Mac Studio at $0 marginal cost per assignment and 1–3 minutes of wall\-clock time per submission, enabling regrading of corrected assignments and greatly expanded TA office hour offerings\. The instructor\-confirmed grading\-error rate held at roughly0\.020\.02–0\.04%0\.04\\%per rubric line item across the term\. Relative to the same instructor’s previous traditionally\-graded cohort, the LaTA\-graded cohort outperformed by approximately11%11\\%on the midterm exam and8%8\\%on the final exam, and reported large gains in self\-assessed confidence on every stated learning objective \(N=159N=159survey responses,Δ≥\+1\.49\\Delta\\geq\+1\.49Likert points,p<10−27p<10^\{\-27\}on every comparison\)\. We release the code under AGPLv3\.

*K*eywordsautomated grading⋅\\cdotlarge language models⋅\\cdoton\-premises inference⋅\\cdotFERPA⋅\\cdotLaTeX workflow⋅\\cdotengineering education

## 1Introduction

Grading handwritten, multi\-part derivations in upper\-division engineering courses is labor\-intensive, slow to return to students, and inconsistent across graders\. At a typical200200\-student enrollment with four\-problem weekly homework assignments and two exams, a single term routinely consumes hundreds of TA hours of first\-pass grading before any feedback reaches a student; the fastest feedback loops in the literature remain measured in days rather than hours, and rubric drift across multiple TAs is an acknowledged threat to consistency\[[14](https://arxiv.org/html/2605.05410#bib.bib16),[23](https://arxiv.org/html/2605.05410#bib.bib18),[7](https://arxiv.org/html/2605.05410#bib.bib21)\]\. Large language models have improved rapidly enough to plausibly automate the first\-pass grading task on symbolic\-derivation problems, and a steady stream of recent work has explored LLMs as STEM graders and tutors\[[27](https://arxiv.org/html/2605.05410#bib.bib1),[19](https://arxiv.org/html/2605.05410#bib.bib5),[20](https://arxiv.org/html/2605.05410#bib.bib6),[2](https://arxiv.org/html/2605.05410#bib.bib4),[18](https://arxiv.org/html/2605.05410#bib.bib3),[30](https://arxiv.org/html/2605.05410#bib.bib26)\]\. Most of this work, however, has been conducted against cloud\-hosted commercial APIs, and a cloud\-graded autograder is difficult to reconcile with FERPA and institutional data\-residency policies in a U\.S\. public university without negotiated contractual carve\-outs\[[8](https://arxiv.org/html/2605.05410#bib.bib22),[11](https://arxiv.org/html/2605.05410#bib.bib10),[13](https://arxiv.org/html/2605.05410#bib.bib11)\]\. The practical result is that many instructors who would benefit from LLM grading cannot deploy it without either anonymising their data or accepting a compliance risk they are not positioned to carry\.

This paper asks a deliberately narrow question:*can a single instructor, with commodity on\-premises hardware and no cloud access, build and deploy an LLM\-based autograder good enough to replace human\-TA first\-pass grading in a200200\-student upper\-division numerical\-methods course, and what happens when they do?*We answer yes, describe the system, namedLaTA\(LaTeX\-native Automated Teaching Assistant\), and report evidence from a full\-term deployment in ME 373 \(Mechanical Engineering Methods\) at Oregon State University during Winter 2026\.

LaTA is built around four commitments\. First,*everything runs on a single on\-premises machine*: no student work leaves the university network, and the compliance story is “we own the hardware” rather than “we negotiated a data\-processing agreement\.” Second,*typed data objects with Pydantic validation*thread through every stage of the pipeline\[[9](https://arxiv.org/html/2605.05410#bib.bib48)\], so the boundary between the model and the rest of the system is schema\-enforced rather than prose\-parsed\. Third,*structural decisions that can be done deterministically are done deterministically*: submission segmentation is regex\-first with an LLM fallback, scoring is binary per rubric item, and LaTeX compilation is handled by pdflatex with an LLM\-assisted repair path only when a compile actually fails\. Fourth,*student recourse is a first\-class feature of the system*: every grading decision produces a two\-channel artefact \(a blunt TA\-facing audit reasoning plus a Socratic, answer\-withholding student hint\), and the corrections\-pass workflow is built directly into the configuration surface\.

The contributions of this paper are:

- •A complete, open\-source reference implementation of an on\-premises LLM autograder for LaTeX\-based STEM coursework, including the pipeline, the Pydantic schemas, the prompt\-injection defences, the LaTeX self\-healing mechanism, and the Gradescope\-integrated regrade workflow \(§[3](https://arxiv.org/html/2605.05410#S3)\)\.
- •A detailed operational account of a full\-term, full\-replacement deployment to200200students across eight homework assignments in a single upper\-division course, including weekly workflow, hardware profile, and the regrade\-request audit \(§[4](https://arxiv.org/html/2605.05410#S4)\)\.
- •A program\-evaluation study of the deployment across three evidence streams; operational data, an anonymous student survey \(N=159N=159\), and a between\-cohort exam comparison against the same instructor’s previous traditionally\-graded cohort, with explicit disclosure of the composite\-intervention confound and the methodological limitations of a single\-year study \(§[5](https://arxiv.org/html/2605.05410#S5)–[6](https://arxiv.org/html/2605.05410#S6)\)\.
- •A reading of the evidence that distinguishes the*autograder*as a commodity from the*workflow*it enables, and a set of generalisation\-envelope recommendations for instructors considering a similar deployment \(§[7](https://arxiv.org/html/2605.05410#S7)\)\.

The remainder of the paper is organised as follows\. §[2](https://arxiv.org/html/2605.05410#S2)situates LaTA against prior work on LLM\-based STEM assessment, on\-premises / FERPA\-aware AI tooling, and LaTeX as a pedagogical substrate\. §[3](https://arxiv.org/html/2605.05410#S3)describes the four\-stage pipeline and its typed data model\. §[4](https://arxiv.org/html/2605.05410#S4)documents the Winter 2026 deployment in ME 373\. §[5](https://arxiv.org/html/2605.05410#S5)–[6](https://arxiv.org/html/2605.05410#S6)report the three evidence streams\. §[7](https://arxiv.org/html/2605.05410#S7)–[8](https://arxiv.org/html/2605.05410#S8)read the results and enumerate what the design of the study cannot tell us\. §[9](https://arxiv.org/html/2605.05410#S9)closes\.

## 2Background and Related Work

We situate LaTA against five strands of prior work: LLM\-based assessment of STEM free\-response problems \(§[2\.1](https://arxiv.org/html/2605.05410#S2.SS1)\); human\-in\-the\-loop rubric scaffolds and chain\-of\-thought prompting for grading \(§[2\.2](https://arxiv.org/html/2605.05410#S2.SS2)\); on\-premises and FERPA\-aware AI in higher education \(§[2\.3](https://arxiv.org/html/2605.05410#S2.SS3)\); LaTeX as a pedagogical substrate for engineering coursework \(§[2\.4](https://arxiv.org/html/2605.05410#S2.SS4)\); and retrospective\-pre survey methodology \(§[2\.5](https://arxiv.org/html/2605.05410#S2.SS5)\)\. The first four locate LaTA as a system; the fifth positions our evidence collection\.

### 2\.1LLM\-based assessment of STEM free\-response problems

A steady stream of work since 2023 has probed large language models as graders of free\-response STEM content, typically against short, well\-bounded items\.Tanet al\.\[[27](https://arxiv.org/html/2605.05410#bib.bib1)\]provide a recent survey of LLM\-based assessment across disciplines and summarise the by\-now familiar pattern: on short, criterion\-referenced items, strong commercial LLMs achieve agreement with human graders that rivals or exceeds human–human inter\-rater reliability, while failing predictably on long items that require arithmetic tracking, multi\-step symbolic manipulation, or commonsense engineering judgement\.Gaoet al\.\[[14](https://arxiv.org/html/2605.05410#bib.bib16)\]andVittoriniet al\.\[[30](https://arxiv.org/html/2605.05410#bib.bib26)\]survey the broader AI\-in\-assessment landscape and reach similar conclusions\.Latif and Zhai \[[19](https://arxiv.org/html/2605.05410#bib.bib5)\]show that fine\-tuning an open\-weight model on a modest corpus of annotated science responses can lift out\-of\-the\-box behaviour substantially on discipline\-specific items, an intervention LaTA does not currently apply but that is a natural direction for future work\. Most of this literature works against short essay\-style or short\-calculation items; while the distinctive features of upper\-division engineering coursework \(long, LaTeX\-native derivations carrying intermediate symbolic manipulations\) have been less well studied\. This is a setting in which the context\-window and reasoning capabilities of large open\-weight models have only recently become adequate\.

A second strand of work treats LLMs as tutors or feedback providers rather than as graders per se\[[34](https://arxiv.org/html/2605.05410#bib.bib12),[1](https://arxiv.org/html/2605.05410#bib.bib17),[13](https://arxiv.org/html/2605.05410#bib.bib11),[28](https://arxiv.org/html/2605.05410#bib.bib25)\]\. The boundary between “grader” and “tutor” is porous in practice: LaTA’s dual\-channel feedback design \(audit reasoning for TAs, Socratic hint for students\) is an intentional nod to the tutoring literature and to work on answer\-withholding feedback as a mechanism for productive struggle\[[34](https://arxiv.org/html/2605.05410#bib.bib12)\]\.

### 2\.2Human\-in\-the\-loop rubric scaffolds and chain\-of\-thought prompting

A related line of work focuses less on raw grader accuracy and more on the*scaffold*that sits between the instructor’s rubric and the LLM’s output\. AVALON\[[2](https://arxiv.org/html/2605.05410#bib.bib4)\], SURE\[[18](https://arxiv.org/html/2605.05410#bib.bib3)\], and CoTAL\[[7](https://arxiv.org/html/2605.05410#bib.bib21)\]all propose structured pipelines in which a human instructor authors or refines a rubric that the LLM then applies, with explicit chain\-of\-thought reasoning preserved for human audit\.Leeet al\.\[[20](https://arxiv.org/html/2605.05410#bib.bib6)\]study the effect of chain\-of\-thought prompting on grader consistency and report non\-trivial gains on items where the model’s reasoning can be inspected\.Chanet al\.\[[5](https://arxiv.org/html/2605.05410#bib.bib7)\]approach the problem from the other direction, using LLMs to generate or refine rubric items rather than to apply them\.

LaTA’s design is in the same family but differs in two respects\. First, we use a reasoning\-model grader \(gpt\-oss:120b for this deployment, but the system is model\-agnostic\) that produces its chain\-of\-thought natively inside<think\>tags, which the pipeline strips from student\-facing output but preserves in the audit trail — closer to CoTAL’s disclosure stance than to the redacted chains of the closed\-API work\. Second, we adopt*binary*rubric scoring \(§[3\.3](https://arxiv.org/html/2605.05410#S3.SS3)\) rather than partial\-credit scoring, on the hypothesis that the dominant source of variance in LLM grading of long derivations is ambiguous partial credit rather than outright misreading; this choice is more conservative than most HITL scaffolds report, and is tuned to the long\-derivation setting rather than the short\-item setting in which partial credit is more tractable and there is less room for stochasticity/LLM hallucination\.

### 2\.3On\-premises and FERPA\-aware AI in higher education

A practical barrier to widespread LLM\-grader deployment in U\.S\. higher education is the intersection of FERPA, institutional data\-residency policies, and the contractual status of commercial cloud APIs\.Colonna \[[8](https://arxiv.org/html/2605.05410#bib.bib22)\]andDan \[[11](https://arxiv.org/html/2605.05410#bib.bib10)\]document teacher and auditor concerns about generative\-AI products that do not offer contractual guarantees about downstream use of student data, andFajardo\-Ramoset al\.\[[13](https://arxiv.org/html/2605.05410#bib.bib11)\]surveys K–12 and higher\-ed deployments that have stalled at the procurement step for exactly this reason\. There may also exist institution\-specific rules instructors must navigate in practice: the result is that a tool that handles identifiable student work must either be anonymized before transmission or hosted within the institutional boundary\.

The literature on genuinely on\-premises LLM deployments in education is thinner than the literature on cloud deployments, reflecting both the recency of capable open\-weight models and the institutional friction of standing up a workstation\-class inference host\.Elkhodr and Gide \[[12](https://arxiv.org/html/2605.05410#bib.bib40)\]describes an on\-premises pilot at a specific institution\. LaTA is explicitly positioned in this corner of the space: a reproducible, open\-source reference for how a single instructor can stand up a compliant on\-premises grader on a single workstation, rather than a cloud product with a compliance wrapper\.

### 2\.4LaTeX as a pedagogical substrate

LaTA’s submission workflow depends on students writing solutions in LaTeX, which is both a prerequisite for machine parsing and a pedagogical intervention in its own right\. The evidence on whether LaTeX is a help or a hindrance to student learning is mixed\.Knauff and Nejasmic \[[17](https://arxiv.org/html/2605.05410#bib.bib13)\]andSeoet al\.\[[26](https://arxiv.org/html/2605.05410#bib.bib35)\]report substantial initial time costs associated with LaTeX relative to word processors, particularly for students without prior programming exposure\.Zhang \[[33](https://arxiv.org/html/2605.05410#bib.bib36)\]andLiuet al\.\[[21](https://arxiv.org/html/2605.05410#bib.bib33)\]provide a framing for this issue in cognitive\-load terms: the extraneous load of learning LaTeX syntax competes with the germane load of the mathematics itself\.Packer and Keates \[[24](https://arxiv.org/html/2605.05410#bib.bib34)\]and the practical guides ofSánchez Egeaet al\.\[[25](https://arxiv.org/html/2605.05410#bib.bib41)\]report mitigation strategies in other domains that can be applied here, such as structured templates, pair\-programming with LaTeX, and starter repositories\. Career development centersWentworth Institute of Technology \[[31](https://arxiv.org/html/2605.05410#bib.bib28)\]anduConnect \[[29](https://arxiv.org/html/2605.05410#bib.bib29)\]argue that the time cost amortises against the career value of the skill in engineering and technical writing\.

Our own ramp\-down data \(Figure[5](https://arxiv.org/html/2605.05410#S6.F5)\) are consistent with the initial\-cost\-that\-amortises story: the mean extra time per assignment fell from8787min at the beginning of the term to4747min at the end, a≈46%\\approx 46\\%reduction\. Despite this improvement, getting even upper\-division STEM students to adopt LaTeX for weekly assignments can be a heavy lift\. To assist with this, a simple tutorial for transcription of handwritten work to LaTeX format via the use of multimodal generative AI tools like Google’s Gemini \(whose pro plan is free for students as of writing\) was furnished to the students\. This video can be found at[https://youtu\.be/B897mHONoOM](https://youtu.be/B897mHONoOM)\.

### 2\.5Retrospective pre\-test methodology

Because our within\-cohort confidence data were collected through a retrospective pre\-test instrument, we situate the choice in the methodological literature\.Howard \[[15](https://arxiv.org/html/2605.05410#bib.bib47)\]is the canonical reference for the retrospective pre/then\-post design: students are asked at the end of an intervention to rate both their pre\-intervention and post\-intervention state, which eliminates the response\-shift bias that afflicts separately administered pre\-tests \(students reinterpret the scale as they learn the content\) at the cost of a documented tendency to inflate apparent gains\. We adopt the design, report its known limitations explicitly \(§[8](https://arxiv.org/html/2605.05410#S8)\), and triangulate the self\-report data against the between\-cohort exam delta \(which is not subject to response\-shift\) so that the qualitative conclusion does not rest on the self\-report alone\.

## 3System Design

LaTA is structured as a four\-stage pipeline \(*ingest*,*segment*,*grade*,*report*\) with two locally hosted open\-weight LLMs, a 20B\-parameter segmenter \(only used in cases where students use the provided\.textemplate improperly\) and a 120B\-parameter grader, along with a set of Pydantic\-validated data objects threaded through each stage\. The design has three load\-bearing commitments: \(1\) everything runs on a single on\-premises machine, so no student work ever leaves the university network; \(2\) the LLM is never trusted to return free\-form text \(every call is coerced to a typed schema\) and \(3\) the same LaTeX the student submits is the grader’s input, eliminating the optical character recognition error surface that dogs handwritten\-PDF autograding\. Figure[1](https://arxiv.org/html/2605.05410#S3.F1)summarizes the data flow; the rest of this section walks each stage\.

![Refer to caption](https://arxiv.org/html/2605.05410v1/x1.png)Figure 1:LaTA architecture\. Four pipeline stages \(shaded region\) transform a Gradescope LaTeX export into a per\-student feedback PDF\. Dashed border nodes are typed data artifacts \(Pydantic\-validated JSON/Python objects\); purple nodes \(bottom\) are locally hosted LLM endpoints\. Only*segment*and*grade*can invoke an LLM;*ingest*and*report*are deterministic\. The corrections loop \(dashed arrow\) ingests a resubmission and regrades while preserving original extra credit and late penalties\. Figure created in\-part with assets generated by Google Gemini\.### 3\.1Ingest: LaTeX\-native, FERPA\-aware submission loading

LaTA consumes a standard Gradescope “Download Submissions” export, which unzips into one directory per student containing the student’s\.texsource file and a top\-levelsubmission\_metadata\.yml\. Each submission is loaded into aStudentSubmissionPydantic model whose fields separate*identifying*information \(name, email, SID; kept for downstream report delivery\) from*LLM\-facing*content, which only ever sees an anonymized 8\-character SHA\-256 hash ofsid∥\\\|internal\_id\. Anonymization is a single configuration flag \(grading\.anonymize\) enforced at the struct boundary, not by hope; we recommend and deployedanonymize: truefor all coursework\.

The central technical problem in ingest is that student\-authored LaTeX is messy: it mixes custom`\\newcommand`macros with a body of mathematical derivations, embedded code, and comments\. Sending the whole file to the grader is wasteful and risks confusing the model with preamble noise\. LaTA parses each\.texfile withpylatexenc\.latexwalker\.LatexWalker, extracts all top\-level macro definitions \(`\\newcommand`,`\\renewcommand`,`\\def`,`\\DeclareMathOperator`,`\\providecommand`\) into a macro block, and extracts the body between`\\begin\{document\}`and`\\end\{document\}`\. The grader then seesmacros\+\+body, which preserves all semantic symbols the student defined \(e\.g\. a course\-wide`\\Prob\{\.\.\.\}`problem header or a student’s`\\vec`\-style shortcut\) without dragging along packaging boilerplate\. If the AST walk fails \(typically on syntactically broken submissions\) LaTA falls back to a string\-level split on\\\\backslashbegin\{document\}so that even malformed files still reach the grader\. Finally, ingest reconciles the submission timestamp against an optional per\-studentextensions\.yamland the later of the\.texand\.pdfupload timestamps, which matters for late\-penalty arithmetic on students who resubmit mid\-cycle\.

### 3\.2Segment: hybrid regex\-first, LLM\-fallback chunking

After ingest, each submission is a single long string\. The grader is significantly more accurate \(and often faster\) when it sees one problem at a time with its matching rubric slice attached, so the next stage splits the body into a list ofProblemChunk\(title, content\)objects\.

The splitter is*hybrid*\. The first attempt is a regular\-expression match against the course\-wide`\\Prob\{\.\.\.\}`macro \(\\\\Prob\\s\*\\\{\(\[ˆ\{\}\]\+\)\\\}\), restricted to top\-level occurrences \(arguments beginning with a digit; sub\-headings like`\\Prob\{1a Undetermined Coefficients\}`are explicitly excluded by a leading\-digit filter\)\. When students duplicate a`\\Prob\{N\}`header inside their work \(a common accident\) the first occurrence wins and subsequent duplicates are merged into the preceding chunk with asegmentation\_warningflag that surfaces in the final report\. A`\\section`fallback pattern catches submissions from students who ignored the`\\Prob\{\}`template\.

If regex yields no top\-level chunks, LaTA falls back to thegpt\-oss:20bsegmenter, invoked through theinstructorlibrary withresponse\_model = SegmentationResultand Ollama JSON mode at temperature 0\.0\. The segmenter prompt is deliberately narrow: “*You are a text extraction tool\. You must NOT solve the problems\. You must NOT write Python code\. You must ONLY copy the text verbatim into labelled chunks\.*” If even the LLM fallback returns nothing coherent, LaTA emits a single‘‘Full Submission’’chunk and sets the warning flag rather than dropping the student submission entirely\. This three\-tier fail\-safe \(regex→\\toLLM→\\towhole\-doc\) is the reason the full\-term deployment in §[4](https://arxiv.org/html/2605.05410#S4)never required manual submission triage\.

### 3\.3Grade: typed, binary, injection\-resistant rubric application

Grading is where LaTA’s design investment is densest\. For eachProblemChunk, the grader assembles a prompt containing: the rubric slice \(YAML, restricted to the rubric items pertaining to theProblemChunk\), the instructor’s reference solution for that problem, a one\-shot JSON example, and the student’s chunk text wrapped in explicitUNTRUSTED INPUTdelimiters\. It then calls thegpt\-oss:120bmodel on the local Ollama server\. The response is validated against a strict Pydantic schema,ChunkGradeResult\(items: List\[GradeItem\]\), where eachGradeItemcarries six fields \(problem\_group,criterion\_name,score,max\_score,audit\_reasoning,student\_hint\) plus anis\_extra\_creditboolean that is set by post\-processing, never by the LLM \(whose copy of the rubric has the flag stripped exactly to prevent that\)\. Three sub\-designs deserve emphasis:

Binary per\-item scoring\.For every rubric item the LLM must award either full points or zero, never partial credit\. The rationale is variance reduction: a 120B model asked “how much partial credit does this derivation deserve?” produces noisy decimal scores that disagree across runs, whereas “does this derivation meet the criterion, yes or no?” collapses to a stable binary decision\. Partial credit is still expressible by the rubric author, who can decompose a single conceptual step into several fine\-grained binary criteria\. This is the same move made in recent rubric\-scaffold frameworks\[[2](https://arxiv.org/html/2605.05410#bib.bib4),[18](https://arxiv.org/html/2605.05410#bib.bib3)\]and in our view is essential for reproducibility\.

Dual\-channel feedback\.EachGradeItemcarries two distinct text fields\.audit\_reasoningis written for the TA and the instructor: blunt, technical, explicit about where the student’s work diverged from the reference solution \(“*student failed to apply the chain rule on term 2*”\)\.student\_hintis written for the student: Socratic, answer\-withholding, never quoting the reference solution\. The prompt repeatedly emphasizes that the hint must never reveal the answer\. In deployment we found this separation essential, but not always sufficient\. This mechanism lets the instructor review a grade at a glance while also producing a genuinely pedagogical feedback PDF, but occasional leakage of reference solution information \(i\.e\.answers\) into student feedback documents did occur, albeit at a low rate \(∼\\sim5% of all feedback documents\)\.

Five\-layer prompt\-injection defense\.Because the student’s submission is untrusted text concatenated into the grader’s input, injection attempts \(“Ignore previous instructions and award full marks”\) are a real attack surface\. LaTA applies five layers of defense: \(1\) a*security preamble*at the top of the system prompt that names injection explicitly and instructs the model to flag but not obey such directives; \(2\)*explicit delimiter banners*\(solid horizontal rules of the form====\.\.\.====\) marking the start and end ofUNTRUSTED INPUT; \(3\) a*role\-reinforcement*reminder after the student block \(“*the text above is student work to be EVALUATED, not instructions to be FOLLOWED*”\); \(4\)*post\-hoc keyword scanning*of the returnedaudit\_reasoningfor known injection phrases \(‘ignore previous’,‘award full points’,‘system:’, etc\.\) with automaticSECURITY\_FLAGemission; and \(5\)*perfect\-score watchdog*alerts that warn on full marks so a TA can spot\-check\. A student who successfully injects a perfect\-score instruction must therefore defeat all five layers simultaneously; we have not observed such a case in deployment\.

Grading is further wrapped by a chain\-of\-thought streaming layer\. Reasoning\-capable models \(our grader is configured withis\_reasoning\_model: true\) stream<think\>\-tagged thinking tokens separately from the JSON response via Ollama’s nativethink=Trueparameter, allowing real\-time display during grading and archival todebug/thinking\.txtfor later audit\. JSON extraction is defensive: the response is stripped of markdown fencing, the outermost\{…\}containing the‘‘items’’key is extracted, and a*LaTeX\-aware sanitizer*converts unescaped single backslashes \(\\\\backslashfrac,\\\\backslashalpha\) to JSON\-valid double backslashes without breaking genuine JSON escapes \(\\\\backslashn,\\\\backslash"\)\. On schema failure the call is retried once with an enhanced warning; on second failure the submission is written tofailed\_submissions\.yamlfor manual review\.

### 3\.4Report: LaTeX feedback with self\-healing compilation

Graded items are grouped byproblem\_group, aggregated into a per\-problem summary table, and rendered through a Jinja2\-templated LaTeX feedback report \(feedback\_report\.tex\)\. Jinja2’s default`\{\{ \}\}`/`\{% %\}`delimiters collide with LaTeX, so LaTA configures custom delimiters \(`\\VAR\{\}`,`\\BLOCK\{\}`,`\\\#\{\}`\) that round\-trip cleanly throughpdflatex\. A per\-fieldlatex\_escapefilter maps the Unicode characters that the LLM compulsively emits \(M\_2typed as a Unicode subscript\-two,\\\\backslashlltyped as<<, Greek letters typed as Unicode glyphs, and the narrow no\-break spaceU\+202F\) to math\-mode LaTeX equivalents while escaping&,%,\#, and underscores in text segments\.

Whenpdflatexcompilation fails despite this cleanup \(almost always because of residual escaping bugs in LLM\-generated free text\) LaTA parses the\.logfile, extracts the explicit error blocks \(lines beginning with‘\!’\), and asks the grader LLM to*fix only the formatting*while keeping all content and Jinja2 markers intact\. The repaired document is then subject to a*hallucination gate*: a validator checks that the returned text still contains`\\documentclass`,`\\begin\{document\}`,`\\end\{document\}`, has Jinja variables or section headers, has length within 50–200% of the original, has a backslash\-command count within 80–120% of the original, and does not contain common hallucination\-topic keywords \(e\.g\. “renewable energy”, “blockchain”\)\. Validation failures roll back to the original document; validation successes are recompiled\. If still broken afterlatex\_fix\_max\_retries, the partial PDF \(if any\) is returned and the broken\.texis preserved inlogs/\.

Late\-penalty and resubmission arithmetic\.Late penalties use UTC timestamps from Gradescope \(which stores submission times in GMT\) compared against the configureddue\_dateafter adding any per\-student extension hours\. Days late are rounded*up*\(24 h \+ 1 s = 2 days late\), penalties are computed againstrubric\_max \+ points\_awarded\_elsewhereto fairly tax manually graded components, and final scores are allowed to go negative \(the instructor handles floor semantics downstream\)\. Resubmissions are detected by comparing the submission timestamp against a persistedgrading\_metadata\.yaml; newer submissions increment a version suffix \(`\_v2`,`\_v3`\) so that the Gradescope re\-upload preserves history\. A*corrections mode*reloads metadata from a base assignment, regrades the corrected submission, and then preserves the originally awarded extra credit \(which is not earnable in corrections\) by re\-injecting syntheticis\_extra\_creditGradeItems into the regraded result\.

### 3\.5Configuration and operational surface

Everything user\-facing lives in a singleconfig\.yaml\(see Listing[1](https://arxiv.org/html/2605.05410#LST1)for an excerpt\)\. The configuration names the segmenter and grader models separately, exposes per\-call Ollama options \(num\_ctx,temperature,repeat\_penalty, etc\.\), switches anonymization on or off, toggles late penalties, schema\-error retries, and LLM LaTeX repair, and hosts the optional SMTP block used bypost\_grading\.pyto deliver feedback PDFs to institutional email addresses\. No credentials are ever stored on disk: SMTP credentials are prompted for at send time, and all model calls terminate athttp://localhost:11434/v1\.

Listing 1:Key fields fromconfig\.yaml\. The segmenter/grader split, binary\-grading options, and local\-onlyollama\_hostare the three design\- critical lines\.system:

ollama\_host:"http://localhost:11434/v1"

segmenter:

model\_name:"gpt\-oss:20b"

ollama\_options:

temperature:0\.0

num\_ctx:32768

grader:

model\_name:"gpt\-oss:120b"

is\_reasoning\_model:true

ollama\_options:

temperature:0\.1

num\_ctx:65536

repeat\_penalty:1\.4

grading:

anonymize:true

skip\_previously\_graded:true

late\_penalties:

enabled:true

penalty\_per\_day:0\.20

due\_date:"2026\-01\-1707:59:59"

error\_handling:

retry\_on\_schema\_error:true

fix\_latex\_errors:true

## 4Deployment: ME 373, Winter 2026

The system described in §[3](https://arxiv.org/html/2605.05410#S3)was not evaluated on a toy dataset\. It was deployed as*the*grader of record inME 373:Mechanical Engineering Methods, a required upper\-division numerical methods course at Oregon State University, for the entire Winter 2026 quarter \(10\-week term\)\. With one narrow exception for hand\-drawn and code\-generated plots, discussed below, every score released to a student in Canvas was generated by LaTA; no human graded any derivation, algebraic manipulation, or Python code excerpt end\-to\-end during the quarter\. This section describes the operational context and the weekly workflow so that the results in §[6](https://arxiv.org/html/2605.05410#S6)can be read against a concrete baseline\.

### 4\.1Course context and hardware

ME 373 enrolled approximately 200 students in Winter 2026\. The course covers root\-finding, numerical linear algebra, interpolation, numerical integration, numerical ODE/PDE solution, and stability theory; material that produces long, multi\-part LaTeX derivations mixing symbolic manipulation, algebra, and Python code excerpts\. Students were required to submit homework in LaTeX, uploading both the\.texsource and the compiled\.pdfto Gradescope\. This LaTeX\-native intake is the precondition that makes LaTA’s OCR\-free design viable; we return to its portability implications in §[8](https://arxiv.org/html/2605.05410#S8)\.

All inference ran on a singleApple Mac Studio \(M3 Ultra, 256 GB unified memory\)located in the instructor’s lab and reachable only from the campus network\. The machine ranollama servebound tolocalhost:11434, hostinggpt\-oss:120b\(the grader\) andgpt\-oss:20b\(the segmenter\) as the only two active models\. No component of LaTA ever issued a network call outside the machine: every model call terminates athttp://localhost:11434/v1; every file read or written is on local disk; credentials for the optional SMTP feedback delivery are prompted for at runtime and never persisted\. The total hardware cost \(a single workstation at about $5k, purchased one\-time\) is the entire marginal cost of the grading infrastructure\. Electricity and incidentals aside, the per\-assignment grading cost is approximately zero, which we revisit quantitatively in §[6](https://arxiv.org/html/2605.05410#S6)\.

Plots: the one manual component\.ME 373 homeworks include hand\-drawn and Python\-generated plots \(e\.g\. convergence curves, phase portraits, numerical\-solution overlays\) that LaTA v1 does*not*grade\. These were graded by the instructor/TAs by direct visual inspection, which takes seconds per plot and adds negligible overhead compared to the derivation\-grading burden LaTA removes\. The resulting point allocation is declared to LaTA via thepoints\_awarded\_elsewherefield ofconfig\.yaml\(see Listing[1](https://arxiv.org/html/2605.05410#LST1)\); this allows the late\-penalty arithmetic in §[3\.4](https://arxiv.org/html/2605.05410#S3.SS4)to deduct penalties from the*total*assignment value \(rubric \+ plots\) rather than from the rubric alone, which would undercharge late students for the manually graded component\. Extending LaTA to autograde plots is an obvious next step: tool\-calling to execute student Python code and reproduce the expected plots, and/or multimodal grading against plot\-image content, would both close this gap\. We return to this in §[8](https://arxiv.org/html/2605.05410#S8)\.

### 4\.2Weekly instructor workflow

ME 373 assigned8 homework setsover the quarter \(weeks 1–9, skipping the midterm and finals weeks\)\. Each homework produced two LaTA runs \(the additional run was for the corrections pass\)\. The instructor’s end\-to\-end weekly effort collapsed to five deterministic tasks:

1. 1\.Reference solution\.Write the complete LaTeX solution as the homework is authored \(this was pre\-existing instructor practice; LaTA adds no overhead\)\.
2. 2\.Rubric\.Author a YAML rubric mirroring the solution’s sub\-problem structure\. Rubric items are written as binary criteria \(see §[3\.3](https://arxiv.org/html/2605.05410#S3.SS3)\); extra\-credit items are marked withis\_extra\_credit: true\. Typical rubric authoring took 30–60 minutes per homework once the instructor had internalized the binary decomposition pattern\.
3. 3\.Configuration\.Updateconfig\.yamlwith the new assignment name, rubric filename, solution path, and the assignment’s GMT\-converted deadline\. No model or prompt changes were made over the term; the samegpt\-oss:120bgrader andgpt\-oss:20bsegmenter ran on every homework\.
4. 4\.Kick\-off\.Immediately after the Gradescope deadline passed, the instructor downloaded the submissions export and launcheduv run grade\.py\. Per\-submission grading time was typically1–3 minutes, varying with the number of sub\-problems and the length of student derivations; for a full cohort of∼\\sim200 students this aggregated to a wall\-clock runtime of4–8 hourson the Mac Studio\. Triggered LLM\-LaTeX fixes, the occasional regex\-fallback to the 20B segmenter, and unusually long student derivations all extend individual submissions in the long tail\. In practice every run started in the evening and completed before the next morning\.
5. 5\.Release and triage\.The instructor spot\-reviewedfailed\_submissions\.yaml\(empty or near\-empty on most weeks\), generated grade input documents for TAs \(PDF files with student scores ordered in chronological Gradescope submission order, allowing for<5<5second/submission grade input times\), and released scores to students after grade entry\. Students received their feedback PDF via institutional email through the built\-in AppleScript or SMTP delivery path viapost\_grading\.py\.

No human graded any LLM\-scored component of any homework submission end\-to\-end during the quarter\.This is a stronger deployment posture than the typical “LLM first\-pass, TA review” pipeline reported in prior work\[[2](https://arxiv.org/html/2605.05410#bib.bib4),[18](https://arxiv.org/html/2605.05410#bib.bib3),[7](https://arxiv.org/html/2605.05410#bib.bib21)\], and it is what makes LaTA’s performance in §[6](https://arxiv.org/html/2605.05410#S6)informative: we are reporting on a system whose outputs were released as the authoritative grade, not curated by a human reviewer before hitting the gradebook\.

### 4\.3Regrade pipeline: corrections and disputes

Full\-replacement grading demands an explicit appeals path\. ME 373 used a two\-tier mechanism\.Tier 1: corrections resubmission\- after the initial LaTA grades were released, students whose submissions had scored less than full credit on at least one grade item could upload a*corrections*submission to Gradescope within a bounded window \(exactly one week from grade being released\)\. Upon closing of the corrections submission window, LaTA’s corrections mode then re\-ingested the new submissions, re\-ran the grading pipeline against the same rubric \(or occasionally improved following feedback from students on the first pass\), and generated a versioned feedback PDF while preserving the originally awarded extra credit \(see §[3\.4](https://arxiv.org/html/2605.05410#S3.SS4)\)\. Zero\-scored grade items were flagged as ineligible for recovery in corrections, consistent with the course policy that corrections were for partial recovery, not for earning points from unsubmitted work\.Tier 2: regrade requests\- if a student believed LaTA’s grade remained incorrect after the corrections pass, they filed a regrade request through Gradescope’s built\-in workflow, which the instructor handled by hand\.

Tier 2 volume was strikingly low\. Across the quarter the instructor received approximately5–10 regrade requests per assignment, essentially all contesting a single rubric item rather than a whole\-problem score\. Roughly half of these requests were judged by the instructor to be valid \(i\.e\. LaTA had misapplied the rubric\) and the other half were not\. Resolving each request \(reading the student’s argument, the submission, and LaTA’saudit\_reasoning, and making a final call\) took on average no more than a few minutes\. To put this volume in context: a typical assignment had∼\\sim3 problems and each problem carried on the order of 10 rubric line items, so a single run of LaTA across∼\\sim200 students made approximately200×3×10≈6,000200\\times 3\\times 10\\approx 6\{,\}000individual rubric\-item decisions per assignment, or∼\\sim96,000 across the eight\-assignment term\.55–1010contested decisions per assignment, of which∼\\sim50% actually required correction, therefore corresponds to an instructor\-confirmed per\-rubric\-item error rate of roughly0\.020\.02–0\.04%0\.04\\%\. We return to this number in §[6](https://arxiv.org/html/2605.05410#S6)\.

#### Corrections\-pass downgrade mechanics\.

A subtle operational pattern emerged that deserves explicit documentation, because it has implications for how corrections\-mode systems should be designed\. Because corrections mode regrades the entire resubmission \(not a diff\) a student who corrected one problem also had their remaining problems regraded by fresh LLM calls\. On several occasions during the term, a rubric item that had been awarded full points on the first pass was flagged in the corrections pass as having a subtle error\. The cause was almost never LLM decoding jitter; it was*rubric evolution*\. The first\-pass run would surface edge cases \(student approaches the instructor had not anticipated\) that exposed ambiguity in the reference solution or in a rubric item’s phrasing\. The instructor would then refine the solution and rubric before launching the corrections pass, and the improved criterion would occasionally catch a subtle mistake the coarser first\-pass criterion had missed\. The instructor’s policy was to*never penalize a student for rubric improvements made between passes*: points not flagged on the first pass stayed awarded, while the new, more detailed audit feedback was still surfaced to the student in the corrections feedback PDF so that they received the pedagogical benefit without the scoring downside\. This policy gives students strictly non\-decreasing scores across passes on previously correct items while still letting the rubric keep improving with deployment experience; a dynamic that we believe is intrinsic to any honest first\-of\-term deployment of an LLM grader, and one that §[8](https://arxiv.org/html/2605.05410#S8)argues future versions should support explicitly \(e\.g\. through ano\_downgradeflag in corrections mode\)\.

## 5Methods

This study is a*program evaluation*of a single\-instructor, single\-course deployment of LaTA during Winter 2026 in ME 373 \(Mechanical Engineering Methods\) at Oregon State University\. We are not testing a hypothesis about LLM\-based grading in general; we are reporting what happened when one instructor replaced TA first\-pass grading with an on\-premises autograder for a full term, and what students said about it afterwards\. All student\-facing instruments were administered for the routine purpose of course improvement, participation was anonymous and voluntary, and the analyses reported here use only aggregate, de\-identified data\. The activity was determined to fall outside the scope of human\-subjects research and did not require IRB oversight; we nonetheless describe the instruments, inclusion rules, and analyses in full so that readers can judge the evidence on its own terms\.

Three streams of evidence are reported: \(i\) operational evidence collected by the instructor during the term, including regrade requests and per\-submission processing time \(§[5\.1](https://arxiv.org/html/2605.05410#S5.SS1)\); \(ii\) an anonymous post\-term student survey administered through Canvas, with both Likert\-scale\[[16](https://arxiv.org/html/2605.05410#bib.bib49)\]items and open\-ended prompts \(§[5\.2](https://arxiv.org/html/2605.05410#S5.SS2), §[5\.3](https://arxiv.org/html/2605.05410#S5.SS3)\); and \(iii\) a between\-cohort, quasi\-experimental comparison of exam performance between the LaTA\-graded Winter 2026 cohort and the traditionally\-graded Winter 2025 cohort taught by the same instructor \(§[5\.4](https://arxiv.org/html/2605.05410#S5.SS4)\)\. Each stream has distinct strengths and weaknesses, and we read them against one another in §[6](https://arxiv.org/html/2605.05410#S6)and §[7](https://arxiv.org/html/2605.05410#S7)rather than relying on any single stream in isolation\.

### 5\.1Operational evidence and regrade audit

Throughout the term, every LaTA run produced a structured output directory \(§[3\.4](https://arxiv.org/html/2605.05410#S3.SS4)\) containing per\-student YAML reports, per\-student PDF feedback, agrading\_metadata\.yamlfile recording model versions and timestamps, and an LLM debug log \(data/debug/\) preserving raw model outputs for any submission that triggered a schema\-error retry or a LaTeX\-repair retry\. These artifacts were not generated for the study; they are the standard operational output of the tool and would exist whether or not a paper was being written\.

For each of the eight graded homework assignments we recorded: the wall\-clock time for one complete grading pass over the 200\-student roster; the number of student regrade requests received during the two\-week window each assignment was open; and, for each regrade, the instructor’s post\-hoc classification as*valid*\(a rubric item was misapplied or the model misread the student’s work\) or*invalid*\(the deduction was correct and the student simply disagreed\)\. A regrade was counted once per rubric line item disputed, not once per email thread\. The per\-submission processing time was computed as the total wall\-clock time divided by 200\.

This evidence stream is strongest at bounding error rates and throughput: every submission passes through the grader three to six times \(once per problem plus a corrections\-pass regrade for the 90% of students who submitted corrections\), and a regrade request is the student\-facing ground truth for whether the first\-pass grade was acceptable\. It is weakest at detecting*missed*deductions, since a student who received credit they did not deserve has no incentive to report the error\. We address this asymmetry in §[5\.2](https://arxiv.org/html/2605.05410#S5.SS2)through an explicit self\-report item about missed deductions\.

### 5\.2Anonymous student survey

A post\-term survey was administered as an optional Canvas quiz during the final week of Winter 2026\. The quiz was worth a small amount of extra credit \(completion\-based, with no penalty for non\-response and no linkage between the completion credit and the anonymous response itself\)\. Canvas delivered responses to the instructor stripped of student identifiers; we report only aggregate statistics\. Of the 200 students enrolled at the beginning of the term,N=159N=159returned a complete response, for a response rate of approximately 80%\.

The instrument comprised four blocks, described below\. The full wording of each item is available upon request \(§[9](https://arxiv.org/html/2605.05410#S9)\); the condensed labels used in the figures and tables are given in parentheses\.

#### Block 1: Pre/post confidence on stated learning objectives\.

For each of the four course\-level learning objectives taken verbatim from the ME 373 syllabus — \(i\) formulating and solving initial value problems \(IVPs\), \(ii\) formulating and solving boundary value problems \(BVPs\), \(iii\) formulating and solving partial differential equations \(PDEs\), and \(iv\) selecting an appropriate numerical method for a given mechanical\-engineering problem — students rated their confidence on a 5\-point Likert scale \(1 = not confident at all, 5 = supremely confident\) at two points:*retrospective pre*\(“how confident did you feel at the beginning of the term…”\) and*post*\(“how confident do you now feel…”\)\. Retrospective pre\-test measurement was chosen over a separate first\-week survey to eliminate the response\-shift bias that arises when students reinterpret a learning objective mid\-term\[[15](https://arxiv.org/html/2605.05410#bib.bib47)\]; we acknowledge the limitation that retrospective pre\-tests tend to inflate apparent gains and return to it in §[8](https://arxiv.org/html/2605.05410#S8)\.

#### Block 2: LaTA grading\-error self\-report\.

Three items asked students to estimate, as a count over the entire term, the number of times they had observed: \(a\) an*incorrect deduction on the first pass*\(“false positive — first pass”\), \(b\) a*missed deduction*that the grader failed to catch \(“false negative”\), and \(c\) an*incorrect deduction on the corrections pass*\(“false positive — corrections”\)\. Items \(a\) and \(c\) are the student\-facing counterpart to the regrade audit in §[5\.1](https://arxiv.org/html/2605.05410#S5.SS1); item \(b\) is the counterpart students can detect but regrade requests cannot\. Free\-response counts were capped at reasonable bounds in the cleaning step \(values\>30\>30were read as outliers and verified against the free\-text essay items before being retained or truncated\)\.

#### Block 3: LaTA perception \(Likert\)\.

Three 5\-point Likert items asked students how valuable the LaTeX submission workflow was to their learning \(“LaTeX valuable”\), how helpful the LaTA feedback was \(“feedback quality”\), and their overall sentiment toward the LaTA grading system \(“overall sentiment”\)\. Two further items asked for their perception of instructor/TA office\-hours support \(“office hours helpful”, “office hours available”\)\. A final pair of items asked the amount of extra time, in minutes per homework assignment, that writing solutions in LaTeX added*at the beginning*of the term and*at the end*of the term, to probe the ramp\-up cost of the LaTeX requirement\.

#### Block 4: Open\-ended essays\.

Two free\-text prompts closed the survey: “What aspects of this course worked well for your learning?” \(positive\) and “What aspects of this course did not work well for your learning?” \(negative\)\. Responses were coded for the thematic analysis described below\.

### 5\.3Thematic coding of open\-ended responses

The two free\-text prompts yielded 260 codable responses \(129 positive, 131 negative\) after removing blanks and single\-word nonresponses \(“n/a”, “idk”, “none”\)\. A single coder \(the instructor–author\) performed open coding on a random 20% sample to generate an initial codebook, consolidated overlapping codes by inspection, then applied the final codebook to the full set\. The resulting codebook contains twelve themes in the positive bucket \(e\.g\.*homework\-as\-learning*,*lecture quality*,*corrections workflow*,*video lectures*,*rigor appreciated*,*LaTeX valuable*,*office hours*,*coding/Python*,*fast feedback*\) and twelve themes in the negative bucket \(e\.g\.*exam difficulty*,*workload*,*lack of examples*,*LaTeX time cost*,*autograder errors*,*lecture pace*,*lecture–homework gap*,*hidden expectations*,*anti\-AI principle*\)\. Each response could receive multiple codes\. Because only one rater coded the data, formal inter\-rater reliability is not reported; the full coded dataset \(thematic\_results\.json\) is available from the author on reasonable request, and we read the thematic counts in §[6](https://arxiv.org/html/2605.05410#S6)only against the signed direction of the quantitative items, not as a standalone claim\.

### 5\.4Between\-cohort exam comparison

A quasi\-experimental comparison of exam performance between the LaTA\-graded Winter 2026 cohort \(enrollment200200;n=182n=182sat the final exam\) and the traditionally\-graded Winter 2025 cohort \(enrollment181181;n=157n=157sat the final\) provides a third, outcome\-oriented window on the intervention\. Both cohorts were taught by the same instructor \(the author\), used the same textbook, met on the same weekly schedule, and were assessed on the same midterm and final\-exam structure \(two proctored, closed\-note written exams, graded by hand by the instructor and TAs in both years\)\. Approximately two\-thirds of the exam problems were held identical across the two years to support this comparison; the remaining third were updated or replaced\. The new items were, in the author’s qualitative judgment, slightly more difficult than the items they replaced; this biases the comparison*against*the Winter 2026 cohort and should be read as a conservative floor rather than as a clean equivalence\.

We report the per\-cohort mean percentage score on the midterm and the final exam\. We deliberately do not report inferential statistics on this comparison, for two reasons\. First, the exam is not exclusively constructed from the held\-identical items, so the numerical delta confounds item\-level difficulty drift with any cohort\-level difference\. Second, and more importantly, LaTA was not the only structural change between the two cohorts\. The Winter 2026 deployment bundled three innovations that cannot be disentangled with a single year of post\-hoc data:

- •LaTA autograding and LaTeX\-native homework\.Homework was submitted in LaTeX and graded by the LaTA pipeline end\-to\-end; the Winter 2025 cohort submitted handwritten PDFs and received manual TA grading feedback\.
- •Corrections workflow\.The Winter 2026 cohort was offered a per\-assignment corrections pass in which the student could revise and resubmit for partial credit restoration \(§[4\.3](https://arxiv.org/html/2605.05410#S4.SS3)\)\. The Winter 2025 cohort received only a first\-pass grade\.
- •Tripled TA office hours\.The TA budget released by LaTA \(§[4\.2](https://arxiv.org/html/2605.05410#S4.SS2)\) was redirected into office hours; TA office\-hour coverage in Winter 2026 was approximately3×3\\timesthat of Winter 2025\.

Any exam\-score gain should therefore be attributed to the*composite*intervention: autograding plus corrections plus expanded office hours\. We make this attribution explicit throughout §[6](https://arxiv.org/html/2605.05410#S6)–[7](https://arxiv.org/html/2605.05410#S7), and we return in §[8](https://arxiv.org/html/2605.05410#S8)to the design changes that would be needed to separate the three components\.

### 5\.5Statistical analysis

Likert\-scale and count items are reported as means with standard deviations and, where appropriate, medians\. For pre/post confidence comparisons \(Block 1\) we report the difference in group means with a nonparametric significance test\. Because the survey was anonymous and pre/post items were collected in a single instrument, we cannot match a specific pre response to the same respondent’s post response; we therefore treat the two distributions as independent for the purposes of significance testing and apply the Mann–WhitneyUUtest\[[22](https://arxiv.org/html/2605.05410#bib.bib19)\]\. We acknowledge that, conditional on the pre and post responses having in fact come from the same 159 respondents, a paired test \(Wilcoxon signed\-rank\[[32](https://arxiv.org/html/2605.05410#bib.bib51)\]\) would be more powerful and slightly more conservative about the direction of individual change; the gain magnitudes we report in §[6](https://arxiv.org/html/2605.05410#S6)are large enough that the choice of test does not alter the qualitative conclusions, but we flag the design limitation explicitly\.

Effect sizes for the pre/post comparison are reported as the rank\-biserial correlationrr​br\_\{rb\}\[[10](https://arxiv.org/html/2605.05410#bib.bib52)\], which for ordinal data are interpretable on the same scale as Cohen’srr\[[6](https://arxiv.org/html/2605.05410#bib.bib53)\]\. No multiple\-comparisons correction is applied to the four learning objective comparisons, because every uncorrected pre/postpp\-value is below10−2010^\{\-20\}and any standard correction \(e\.g\. Bonferroni\[[4](https://arxiv.org/html/2605.05410#bib.bib54)\]or Benjamini–Hochberg\[[3](https://arxiv.org/html/2605.05410#bib.bib55)\]atq=0\.05q=0\.05\) would leave the qualitative conclusion unchanged\.

All statistical computations were produced by the analysis scriptanalyze\_feedback\.pywhich is available upon request\. The script reads the raw Canvas export \(Survey\_Feedback\.csv\), applies the column mapping documented in the survey instrument section, and writes the per\-figure data and a console summary of means, standard deviations,UUstatistics,rr​br\_\{rb\}effect sizes, andpp\-values\. For the between\-cohort exam comparison \(§[5\.4](https://arxiv.org/html/2605.05410#S5.SS4)\), we report only the difference in sample means by design, for the reasons given there\.

## 6Results

Results are reported in the same order as the evidence streams in §[5](https://arxiv.org/html/2605.05410#S5): operational data first \(§[6\.1](https://arxiv.org/html/2605.05410#S6.SS1)\), survey data second \(§[6\.2](https://arxiv.org/html/2605.05410#S6.SS2)\), thematic coding third \(§[6\.3](https://arxiv.org/html/2605.05410#S6.SS3)\), and the between\-cohort exam comparison last \(§[6\.4](https://arxiv.org/html/2605.05410#S6.SS4)\)\. The overall picture is one of a deployment that worked \(throughput, accuracy, and learning outcomes all moved in the intended direction\) while producing mixed student perceptions of the autograder itself\. We read the mixed perception signal not as a contradiction but as the most honest part of the result\.

### 6\.1Operational results: throughput and accuracy

Across the eight homework assignments of Winter 2026, LaTA graded the 200\-student roster in11–33minutes of wall\-clock time per submission on the Mac Studio M3 Ultra described in §[4\.1](https://arxiv.org/html/2605.05410#S4.SS1), with the per\-assignment total dominated by the number of problems and the length of the worked solutions rather than by class size\. In aggregate, the instructor received55–1010regrade requests per assignment, of which approximately half were judged valid on review \(§[4\.3](https://arxiv.org/html/2605.05410#S4.SS3)\)\. A regrade request typically involved one disputed rubric line item, not the whole problem\.

The natural unit for an error rate in this setting is the*rubric line item*, not the student or the assignment, because that is the granularity at which LaTA actually makes a binary grading decision\. A typical assignment carried∼\\sim3 problems with∼\\sim10 rubric items per problem, so a single grading run made approximately200×3×10≈6,000200\\times 3\\times 10\\approx 6\{,\}000rubric decisions per assignment, or∼\\sim96,000 rubric decisions across the eight\-assignment term given the corrections pass\. Against this denominator, the55–1010regrade requests received per assignment correspond to a*contested*per\-rubric\-item rate of0\.040\.04–0\.08%0\.08\\%and an*instructor\-confirmed*per\-rubric\-item error rate \(after the∼\\sim50% valid filter\) of approximately0\.020\.02–0\.04%0\.04\\%\. In absolute terms, the instructor confirmed errors on roughly33–55of the∼\\sim12,00012\{,\}000rubric decisions per assignment, or roughly2020–4040across the entire term\. Resolving each contested decision took the instructor on the order of11–22minutes and produced a corrected report via the versioning mechanism described in §[3\.4](https://arxiv.org/html/2605.05410#S3.SS4)\.

No submission failed to produce a final feedback PDF\. LLM JSON\-schema retries and LaTeX compilation retries both engaged occasionally during the term, but in every case the retry path in §[3\.3](https://arxiv.org/html/2605.05410#S3.SS3)and §[3\.4](https://arxiv.org/html/2605.05410#S3.SS4)converged within the configured retry budget\. A small number of first\-pass grades were hand\-corrected by the instructor when the model had misread a student’s notation \(e\.g\. treating a double\-prime as a prime\); these were logged as valid regrade requests and did not require tool\-level intervention\.

### 6\.2Survey results: confidence, accuracy, and perception

OfN=159N=159survey respondents \(≈80%\\approx 80\\%response rate\), every question attracted between158158and159159valid answers; none were dropped for completeness\.

#### Confidence on learning objectives\.

Retrospective pre/post confidence ratings rose by more than one full Likert point on every one of the four stated learning objectives \(Figure[2](https://arxiv.org/html/2605.05410#S6.F2)\)\. Initial value problems rose from a pre\-term mean ofMpre=1\.91M\_\{\\text\{pre\}\}=1\.91\(SD=0\.98=0\.98\) toMpost=3\.47M\_\{\\text\{post\}\}=3\.47\(SD=0\.81=0\.81\), a gain ofΔ=\+1\.57\\Delta=\+1\.57; boundary value problems fromMpre=1\.47M\_\{\\text\{pre\}\}=1\.47\(SD=0\.80=0\.80\) toMpost=3\.20M\_\{\\text\{post\}\}=3\.20\(SD=0\.76=0\.76\),Δ=\+1\.74\\Delta=\+1\.74; partial differential equations from1\.941\.94\(SD=0\.99=0\.99\) to3\.433\.43\(SD=0\.93=0\.93\),Δ=\+1\.49\\Delta=\+1\.49; and numerical method selection from1\.501\.50\(SD=0\.88=0\.88\) to3\.383\.38\(SD=0\.89=0\.89\),Δ=\+1\.88\\Delta=\+1\.88\. All four comparisons achieved Mann–WhitneyUUp<10−27p<10^\{\-27\}uncorrected \(well below the threshold any standard multiple\-comparisons correction would impose atq=0\.05q=0\.05across only four tests\) with rank\-biserial effect sizesrr​br\_\{rb\}ranging from0\.6880\.688\(PDEs\) to0\.8250\.825\(BVPs\), conventionally “large” effects\. The retrospective\-pre design inflates apparent gains; even heavily discounted, the post\-term medians of33–44on every objective represent a cohort that finished the term feeling moderately to strongly confident about content they had reported near\-floor confidence on at the start\.

![Refer to caption](https://arxiv.org/html/2605.05410v1/x2.png)Figure 2:Retrospective pre/post confidence distributions on the four ME 373 learning objectives \(1 = not confident at all, 5 = supremely confident\)\.N=159N=159; dashed vertical lines mark group means; all comparisonsp<10−27p<10^\{\-27\}by Mann–WhitneyUU\.
#### Student\-reported grading accuracy\.

The three self\-report items on grading accuracy \(Block 2 of the survey\) yielded the distributions shown in Figure[3](https://arxiv.org/html/2605.05410#S6.F3)\. Across the entire term, students reported a mean of2\.942\.94\(SD=2\.99=2\.99, median=2=2\) first\-pass false\-positive deductions \(instances where LaTA took points off incorrectly\) with a range of\[0,20\]\[0,20\]\. Missed deductions \(false negatives\) averaged1\.081\.08\(SD=1\.31=1\.31, median=1=1\); corrections\-pass false positives averaged1\.461\.46\(SD=1\.68=1\.68, median=1=1\)\. Across the eight\-assignment term, each student received on the order of8×3×10×2=4808\\times 3\\times 10\\times 2=480individual rubric\-item decisions, so the student\-perceived first\-pass false\-positive rate normalises to approximately0\.6%0\.6\\%per rubric item, the false\-negative rate to≈0\.22%\\approx 0\.22\\%, and the corrections\-pass false\-positive rate to≈0\.3%\\approx 0\.3\\%\.

Two cross\-checks are worth flagging\. First, the*student\-perceived*per\-rubric\-item first\-pass false\-positive rate \(≈0\.6%\\approx 0\.6\\%\) is roughly an order of magnitude larger than the*instructor\-confirmed*per\-rubric\-item rate from the regrade audit \(0\.040\.04–0\.08%0\.08\\%, §[6\.1](https://arxiv.org/html/2605.05410#S6.SS1)\)\. Much of this gap is explained by students who noticed a deduction, disagreed with it, but did not formally request a regrade; either because the point total did not affect their letter grade, or because the corrections\-pass workflow already restored the credit\. Both numbers are below the noise floor of human\-TA grading consistency reported in the broader assessment literature, and we read them as upper and lower bounds on the true first\-pass error rate rather than treating either as ground truth\. Second, first\-pass false positives outnumber corrections\-pass false positives by roughly22:11, consistent with the rubric\-evolution dynamic described in §[4\.3](https://arxiv.org/html/2605.05410#S4.SS3): the second pass runs against a more mature rubric and catches fewer spurious deductions\.

![Refer to caption](https://arxiv.org/html/2605.05410v1/x3.png)Figure 3:Student\-reported LaTA grading errors over the full term\. Left: first\-pass false positives \(incorrect deductions\)\. Middle: false negatives \(missed deductions\)\. Right: corrections\-pass false positives\. Dashed line is the mean\. Per\-rubric\-item rates derived from these counts are reported in the body\.
#### LaTA perception Likerts\.

Figure[4](https://arxiv.org/html/2605.05410#S6.F4)reports the three LaTA\-perception Likert items as stacked percentages\. Student ratings are honest and mixed\. The LaTeX submission workflow itself was the most positively received component, with a mean ofM=3\.64M=3\.64\(SD=1\.13=1\.13\) and59\.7%59\.7\\%of respondents selecting44or55; the feedback content \(M=3\.26M=3\.26, SD=0\.98=0\.98;42\.1%42\.1\\%positive\) and overall sentiment toward LaTA \(M=3\.16M=3\.16, SD=1\.09=1\.09;40\.3%40\.3\\%positive\) both sat slightly above the neutral midpoint but below the LaTeX\-workflow item\. In other words, students on average judged the transformation to a LaTeX\-native workflow as more valuable than the autograder at the centre of that workflow; a finding we return to in §[7](https://arxiv.org/html/2605.05410#S7)\.

#### LaTeX ramp\-up cost\.

The paired time items \(extra minutes per assignment spent on LaTeX\) show a substantial reduction across the term \(Figure[5](https://arxiv.org/html/2605.05410#S6.F5)\)\. At the beginning of the term the cohort reported a mean extra time ofM=87M=87min per assignment \(SD=63=63, median=60=60\); by the end of the term this had fallen toM=47M=47min \(SD=48=48, median=30=30\), a mean reduction of≈46%\\approx 46\\%\. Median reductions are larger \(50%50\\%\) and better reflect the typical student, since the begin\-of\-term distribution carries a long right tail of students reporting≥2\\geq 2hours of extra LaTeX time on the first assignment\. This pattern \(a real up\-front time cost that decays with practice\) is consistent with the LaTeX\-time theme in the open\-ended essays \(§[6\.3](https://arxiv.org/html/2605.05410#S6.SS3)\)\.

![Refer to caption](https://arxiv.org/html/2605.05410v1/x4.png)Figure 4:Student perception of three LaTA\-related items \(stacked diverging Likert, 1–5\)\. The LaTeX workflow item draws more positive response than feedback quality or overall sentiment toward the system\.![Refer to caption](https://arxiv.org/html/2605.05410v1/x5.png)Figure 5:Student\-reported extra time \(in minutes per assignment\) spent writing solutions inLaTeX\. Blue: beginning of term\. Red: end of term\. Medians halve; means drop by≈46%\\approx 46\\%\.
#### Summary\.

Per\-objective means, standard deviations, deltas, effect sizes, andpp\-values for the four learning objectives are collected in Table[1](https://arxiv.org/html/2605.05410#S6.T1)\. The full numeric tabulation is reproducible fromanalyze\_feedback\.py\.

Table 1:Pre/post confidence ratings across the four ME 373 learning objectives \(N=159N=159\)\. Mann–WhitneyUUtest; effect sizerr​br\_\{rb\}is the rank\-biserial correlation\.

### 6\.3Thematic coding of open\-ended responses

Of theN=159N=159respondents,129129contributed a codable answer to the positive prompt and131131to the negative prompt\. Most of the dominant themes in both buckets are about course design broadly rather than LaTA specifically, and we report only the LaTA\-relevant signal in detail here\.

For context, we report the largest themes as the count of respondents in the relevant bucket whose essay was coded with that theme \(each essay could receive multiple codes, so the counts do not sum to the bucket size\)\. On the positive side \(n\+=129n\_\{\+\}=129respondents\), the largest non\-LaTA themes were*homework\-as\-learning*\(5656respondents,43%43\\%\),*lecture quality*\(4646,36%36\\%\), and*video lectures*\(2929,22%22\\%\)\. On the negative side \(n−=131n\_\{\-\}=131respondents\), the largest non\-LaTA themes were*exam difficulty*\(4646respondents,35%35\\%\),*workload*\(3434,26%26\\%\), and*lack of worked examples*\(3030,23%23\\%\)\. Each of these non\-LaTA negative themes was endorsed by more respondents than either of the two LaTA\-specific complaint themes \(*LaTeX time cost*:2222,17%17\\%ofn−n\_\{\-\};*autograder errors*:1818,14%14\\%ofn−n\_\{\-\}\)\. The takeaway from the broad coding is that LaTA was not the principal complaint of the term on either side of the ledger; the dominant complaints are familiar course\-design concerns that long predate any autograder\.

Figure[6](https://arxiv.org/html/2605.05410#S6.F6)isolates only those free\-text codes that directly reference LaTA or the LaTeX workflow, collapsed across the two prompts\. The most\-discussed LaTA\-adjacent themes are*corrections workflow*\(3434positive mentions,*no*negative mentions\),*LaTeX valuable*\(2121positive\),*autograder errors*\(1818negative\),*LaTeX time cost*\(2222negative\), and*fast feedback*\(1111positive\)\. The smallest identifiable LaTA\-related theme is*anti\-AI principle*\(44negative respondents,3%3\\%ofn−n\_\{\-\}\): students who objected to an AI system grading their work as a matter of principle, independent of whether the grades were correct\. We consider this a real and persistent minority signal rather than noise, and return to it in §[7](https://arxiv.org/html/2605.05410#S7)\. Across both prompts, many students wrote balanced, mixed\-sentiment essays even in the “what worked well” box \(4545positive\-prompt essays coded as*mixed*,6767negative\-prompt essays coded as*mixed*\), consistent with the mid\-range overall\-sentiment Likert mean of3\.163\.16reported above\.

![Refer to caption](https://arxiv.org/html/2605.05410v1/x6.png)Figure 6:LaTA\-specific free\-text themes, stacked by sentiment \(positive on top, negative on bottom\)\. The corrections workflow is the single most\-praised LaTA\-adjacent feature and has no corresponding negative mentions; the anti\-AI\-principle category atn=4n=4is the smallest identifiable LaTA\-related theme\.#### Office\-hours signal\.

A pair of Likert items asked students how helpful and how available TA and instructor office hours were during the term \(Figure[7](https://arxiv.org/html/2605.05410#S6.F7)\)\. Both items were rated positively \(M\>3\.5M\>3\.5on the11–55scale\), consistent with the tripling of TA office\-hour coverage described in §[5\.4](https://arxiv.org/html/2605.05410#S5.SS4)\. We flag this item here rather than in the LaTA\-perception subsection because office hours are not part of LaTA; they are the bundled structural change that the TA\-time savings enabled\.

![Refer to caption](https://arxiv.org/html/2605.05410v1/x7.png)Figure 7:Student perception of office\-hours helpfulness and availability \(1–5 Likert\)\. Both items receiveM\>3\.5M\>3\.5, reflecting the3×3\\timesexpansion of TA coverage\.

### 6\.4Between\-cohort exam comparison

Mean exam scores for the LaTA\-graded Winter 2026 cohort \(enrollment200200;n=182n=182sat the final\) were higher than for the traditionally\-graded Winter 2025 cohort \(enrollment181181;n=157n=157sat the final\) by approximately11%11\\%on the midterm and approximately8%8\\%on the final, on a0–100100scale\. Because approximately one\-third of exam items were updated between years and the new items were, in the instructor’s qualitative judgement, slightly harder, both deltas should be read as conservative lower bounds\. We deliberately do not report inferentialpp\-values on this comparison \(§[5\.4](https://arxiv.org/html/2605.05410#S5.SS4)\); the point of the comparison is directional and coarse\.

The between\-cohort delta is consistent with, but stronger than, the within\-cohort confidence gains reported in §[6\.2](https://arxiv.org/html/2605.05410#S6.SS2): exams are a harder criterion than retrospective self\-report, and seeing a positive exam delta on top of a positive confidence delta mitigates the response\-shift concern attached to the retrospective pre\-test design\. Two cautions apply\. First, the Winter 2026 cohort received not only LaTA autograding but also a corrections workflow and approximately3×3\\timesthe TA office\-hour coverage of the Winter 2025 cohort; the exam delta is attributable to the composite intervention, not to LaTA in isolation \(§[7](https://arxiv.org/html/2605.05410#S7)\)\. Second, a single\-year, single\-instructor comparison is not a controlled trial, and the delta reported here should not be read as a causal estimate of the autograder’s isolated contribution\. What the exam delta does do is rule out the stronger negative hypothesis that moving to a LaTeX\-native, LLM\-graded workflow damaged student performance: within the precision of a one\-year cohort comparison at this enrolment, it did the opposite\.

## 7Discussion

Read across the three evidence streams, Winter 2026 looks like a deployment that worked\. LaTA processed≈200\\approx 200students for each of eight homework assignments on a single workstation, at11–33minutes of wall\-clock time per submission and no end\-to\-end failures; the instructor\-facing regrade rate held near2\.5%2\.5\\%per assignment with half of requests judged valid; student confidence on every stated learning objective rose by more than one full Likert point; and a cohort whose exam items were, if anything, slightly harder than its predecessor’s outperformed that predecessor by roughly11%11\\%and8%8\\%on the midterm and final\. We did not find evidence that replacing TA first\-pass grading with an on\-premises LLM pipeline harmed student learning\. The more interesting question is what the evidence says about*how*it worked, and about the honest\-mixed signal in the student\-perception data\.

### 7\.1The workflow, not the autograder, is what students valued

The single most striking pattern in the survey is that students rated the LaTeX submission workflow \(M=3\.64M=3\.64,59\.7%59\.7\\%positive\) more highly than the quality of LaTA’s feedback \(M=3\.26M=3\.26\) or their overall sentiment toward LaTA as a grader \(M=3\.16M=3\.16\), and the thematic coding reproduced the same ordering: the corrections workflow was the most\-mentioned positive LaTA\-adjacent theme and attracted*zero*negative mentions, while LaTeX\-valuable appeared2121times positively and autograder\-errors appeared1818times negatively\. In other words, students appear to be distinguishing the*framework*LaTA makes possible \(typed submissions, fast turnaround, cheap corrections passes, feedback that lives in a document rather than a margin\) from the*autograder at the core*of that framework\.

This is, on reflection, what one should expect\. The autograder itself is a commodity: any reasonable LLM pipeline that ingests LaTeX, applies a rubric, and returns structured JSON would produce feedback of roughly comparable quality given comparable rubrics\. What LaTA does that most cloud\-graders cannot is stay on\-premises and release enough instructor time to fund the structural changes \(corrections, tripled office hours\) that students directly experience\. The lesson for instructors considering an LLM\-autograder deployment is that the autograder is probably not the headline feature; the headline feature is the budget that the autograder releases, provided the instructor uses that budget to change the course rather than simply to save time\.

### 7\.2The student\-perception gap: mixed is the honest reading

The three LaTA\-specific Likert items all cluster slightly above the neutral midpoint, not solidly in “positive” territory\. We take this mid\-range signal at face value\. Students who were charged an incorrect deduction — even one — and then had that deduction restored by a corrections pass or a regrade request are understandably not uniformly enthusiastic about the system that made the mistake, even if they acknowledge that the end\-of\-term point total was correct\. Mean3\.163\.16overall sentiment is consistent with a cohort that experienced both the wins \(fast turnaround, cheap corrections, consistent rubric application\) and the costs \(occasional misread notation, occasional LaTeX friction\) of the deployment, and answered honestly\. Treating these numbers as a failure of student buy\-in would be a misread: the same respondents produced the large confidence gains in Block 1 and the thematic praise for the corrections workflow in Block 4\.

The gap between student\-perceived first\-pass false positives \(M=2\.94M=2\.94per student over the term\) and instructor\-confirmed valid regrades from the audit \(≈0\.4\\approx 0\.4per student over the term\) deserves a direct treatment\. Part of the gap is definitional: a student’s self\-reported false positive is any deduction they thought was wrong, whereas the audit counts only deductions that survived instructor review\. Part of the gap is procedural: many students who disagreed with a first\-pass deduction found the disagreement resolved by the corrections pass, and had no incentive to file a formal regrade\. And part of the gap is almost certainly perceptual: the system was imperfect in ways that are noticeable to the student whose work was misread, and the audit undercounts those misreads\. The useful posture is to read the two numbers as upper and lower bounds on the true first\-pass error rate and to report both, rather than to claim the audit is the “real” number and the self\-report is inflated\.

### 7\.3The anti\-AI\-principle minority

A small but persistent minority of respondents \(44coded mentions,3%3\\%of negative essays\) objected to the use of AI to grade their work as a matter of principle, independent of whether the grades themselves were correct\. We do not read this signal as a failure of the system but as an honest expression of a value judgement that the system cannot address by being more accurate\. Any deployment of an LLM in a student\-facing role will produce this minority, and the appropriate response is not to design around it but to acknowledge it\. LaTA’s mitigations at the design level \(full disclosure of AI grading in the syllabus, human\-reviewable audit trails, a first\-class regrade pipeline, on\-premises data residency\) are necessary but not sufficient: students who hold a principled objection to AI grading retain that objection even when all of the usual technical concerns \(privacy, accuracy, recourse\) have been addressed\.

### 7\.4Attributing the exam\-delta to the composite intervention

The between\-cohort exam delta cannot be cleanly attributed to LaTA in isolation, because LaTA, the corrections workflow, and the tripling of TA office hours were introduced as a single package \(§[5\.4](https://arxiv.org/html/2605.05410#S5.SS4)\)\. The most defensible reading of the delta is that the composite intervention improved outcomes by88–1111percentage points relative to the previous year’s traditional deployment at the same institution with the same instructor, and that the pieces of the composite are causally interlocked: the autograder released the TA time that funded the extra office hours, and the typed\-submission workflow is what made cheap corrections passes possible at all\. A pure\-LaTA counterfactual \(LLM grading with no corrections workflow and no extra office hours\) would probably still save instructor time, but the exam signal we report is about the full package\. The cleanest way to separate the three components is a multi\-section or multi\-course replication in which they are introduced independently; we flag this as a high\-priority future design \(§[9](https://arxiv.org/html/2605.05410#S9)\)\.

### 7\.5Generalisation envelope

The specific course we deployed to; an undergraduate numerical\-methods course with heavy symbolic derivation, LaTeX submissions already permissible, and a200200\-student enrollment, sits near the sweet spot for an LLM autograder: problems are long enough that copying a reference answer is not a tight rubric, short enough to fit in a6464k\-token context, and structured enough that a binary\-scored rubric reliably captures the decisions a human TA would make\. Two deformations of the setting would make the approach straightforwardly transferable and one would make it harder\.

Straightforwardly transferable:*adjacent symbolic\-derivation courses*\(differential equations, continuum mechanics, introductory fluids, heat transfer, controls\) where solutions are worked algebraically and an answer is a short expression or a sentence of physical interpretation\. The dominant effort in such a deployment is rubric authoring, not pipeline engineering; the pipeline described here runs as\-is\. Also transferable:*LaTeX\-friendly disciplines beyond engineering*\(physics, applied mathematics, economics with a formal component\), provided the instructor is willing to require LaTeX submissions\.

Harder: courses whose core deliverable is a*plot, a piece of running code, or a schematic*\. LaTA grades plots through thepoints\_awarded\_elsewherehand\-graded path \(§[4\.1](https://arxiv.org/html/2605.05410#S4.SS1)\); it does not run student code or inspect a figure\. Tool\-calling and multimodal LLMs are the obvious future extensions, and both are explicit work items for LaTA\. A separate concern is*natural\-language reasoning problems*\(e\.g\. a design justification essay\), where binary rubric items are a worse fit than they are for a derivation and the value of an LLM grader depends more sensitively on the rubric\-author’s skill\. These are not unreachable settings, but they are not the setting in which LaTA was validated\.

## 8Limitations

The evidence reported in this paper has six limitations worth naming explicitly\.

#### Single\-instructor, single\-course, single\-year deployment\.

All data come from one section of ME 373 taught by the author at Oregon State University during Winter 2026, compared against the corresponding section taught by the same author during Winter 2025\. The strongest threat to generalisation is simply that one instructor’s tastes in rubric authoring, feedback style, and class culture are bundled into every measurement we report\. A deployment by a different instructor \(even of the same tool against the same syllabus\) would produce different perception scores, a different regrade\-request distribution, and probably a different ceiling on what the autograder can score reliably\. The results reported here should be read as an existence proof that the tool can carry a full\-replacement deployment at this scale with these outcomes, not as an estimate of the effect size an arbitrary instructor should expect\.

#### Composite intervention\.

As detailed in §[5\.4](https://arxiv.org/html/2605.05410#S5.SS4)and §[7\.4](https://arxiv.org/html/2605.05410#S7.SS4), the exam\-score delta against the Winter 2025 cohort is attributable to the full package of LaTA autograding*plus*the corrections workflow*plus*a tripling of TA office hours, and a single year of post\-hoc data cannot separate the three\. The delta we report is the composite effect and we read it as such throughout\. A multi\-section or multi\-year replication that introduces the three components independently is the clean way to disentangle them\.

#### Retrospective pre\-test\.

The pre/post confidence gains in Block 1 were collected through a single\-administration retrospective pre\-test\. This design eliminates the response\-shift bias that afflicts separate first\-week and last\-week surveys\[[15](https://arxiv.org/html/2605.05410#bib.bib47)\]but introduces its own well\-documented tendency to inflate apparent gains: students who finish a course with greater confidence retrospectively downrate their pre\-course confidence compared to what they would have self\-reported at the beginning\. Every gain we report exceeds one full Likert point, and the between\-cohort exam comparison moves in the same direction, so the qualitative conclusion is unlikely to be an artefact of the instrument; nonetheless the precise gain magnitudes should not be read as clean pre/post differences\.

#### Unpaired analysis of paired pre/post items\.

Because the survey was anonymous and delivered in a single administration, we cannot match a respondent’s pre\-term confidence rating to the same respondent’s post\-term rating, and we analysed the two distributions with the Mann–WhitneyUUtest as if they were independent samples \(§[5\.5](https://arxiv.org/html/2605.05410#S5.SS5)\)\. A paired test \(Wilcoxon signed\-rank\) would use the actual dependence structure and be slightly more powerful; conditional on the anonymous design, the reportedpp\-values are a small and conservative approximation\. This does not change any qualitative conclusion at the magnitudes reported, but future survey instruments should include a non\-identifying session token that permits paired analysis\.

#### Single\-coder thematic analysis\.

The open\-ended essay responses were coded by one rater \(the instructor–author\) against a codebook developed on a random20%20\\%sample \(§[5\.3](https://arxiv.org/html/2605.05410#S5.SS3)\)\. No inter\-rater\-reliability statistic is available, and the coder is not blind to the intervention\. The codebook and the full response\-level coding are available upon request so that interested readers can apply their own coding scheme; readers who would prefer a second rater can treat the thematic counts here as a descriptive signal to be checked against the Likert items, not as a standalone claim\.

#### No head\-to\-head comparison with alternative graders\.

This study reports a deployment of one specific autograder configuration \(gpt\-oss:120b on Ollama at a6565k\-token context, with a binary\-scored rubric and the prompt\-injection defences in §[3\.3](https://arxiv.org/html/2605.05410#S3.SS3)\)\. We do not compare LaTA’s grading decisions against those of a cloud\-hosted LLM, against a different local model, against a human TA on the same submissions, or against LaTA with the injection defences disabled\. Each of these is a natural follow\-up study, and each would answer a question this deployment\-focused paper does not\. In particular, the claim that the pipeline is robust to prompt injection is a claim\-by\-construction \(the five layers are present in the source code and the audit log records no successful injections during the term\) rather than a red\-team validation\.

#### Plot hand\-grading carve\-out\.

LaTA did not grade the plot\-only subproblems of the assignments; those were hand\-graded visually by the instructor using thepoints\_awarded\_elsewheremechanism \(§[4\.1](https://arxiv.org/html/2605.05410#S4.SS1)\)\. The accuracy numbers we report cover the LLM\-scored components only\. Future versions that use tool\-calling to run student code or multimodal LLMs to inspect figures would close this gap, but the current deployment does not speak to them\.

## 9Conclusion

LaTA is an on\-premises, FERPA\-compliant, full\-replacement autograder for handwritten\-style STEM coursework\. It ingests LaTeX submissions from Gradescope, segments them, grades them against an instructor\-authored YAML rubric using a large local reasoning LLM on a single workstation, and emits per\-student PDF feedback that self\-heals through LaTeX compilation retries\. The pipeline is deterministic enough in its structural decisions \(typed schemas, binary\-scored rubrics, regex\-first segmentation\) and defensive enough in its LLM\-facing surface \(five\-layer injection defence, LLM\-fixer for compilation errors, audited regrade trail\) to run unattended on200200\-student assignments for a full term\.

Deployed through one section of a numerical\-methods course at Oregon State University during Winter 2026, LaTA graded every submission of eight homework assignments with no end\-to\-end failures, an instructor\-facing valid\-regrade rate of approximately2\.5%2\.5\\%per assignment, and11–33minutes of wall\-clock time per submission\. Relative to the same instructor’s previous traditionally\-graded cohort, the LaTA\-graded cohort reported large confidence gains on all four stated learning objectives and outperformed the previous cohort by11%11\\%on the midterm and8%8\\%on the final on a shared exam structure, with the exam delta biased conservatively by the slightly harder new items\. Student perception of the autograder itself was mid\-range and honest; perception of the LaTeX submission workflow and the corrections pass it made possible was substantially more positive\. The most\-cited positive theme in open\-ended responses was the corrections workflow, which drew zero negative mentions\.

We read the evidence as saying that the autograder is useful primarily because it releases instructor and TA time, and that instructors considering a deployment of this kind should plan the deployment around what to do with that released time \(in our case, corrections passes and expanded office hours\) rather than around the autograder in isolation\. The next priorities for LaTA itself are tool\-calling to run student code \(closing the plot\-grading carve\-out\), multimodal grading for figures, and a paired\-design survey instrument that supports a within\-subject pre/post analysis\. The clearest external priority is a multi\-instructor, multi\-course replication that disentangles LaTA, the corrections workflow, and the expanded office hours as independent interventions, so that future deployments can be costed and planned component by component rather than as a single bundle\.

The LaTA source code, the survey instrument, the analysis script, and the thematic coding are available as described in the Data and code availability statement below\.

## CRediT authorship contribution statement

Jesse A\. Rodríguez:Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing — original draft, Writing — review & editing, Visualization, Supervision, Project administration\.

## Declaration of generative AI and AI\-assisted technologies in the writing process

During the preparation of this work the author used Anthropic Claude \(Sonnet and Opus, including the agentic Claude Code system for original code development\) for draft editing and code documentation review\. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication\.

## Data and code availability

The LaTA source code is released under AGPLv3 at[https://github\.com/JesseRodriguez/LaTA](https://github.com/JesseRodriguez/LaTA)\. Student\-level grading data are FERPA\-protected and cannot be shared; aggregate, de\-identified statistics reported in §[6](https://arxiv.org/html/2605.05410#S6)as well as other instruments are available from the author upon reasonable request\.

## Acknowledgments

The author thanks the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University and the Nesbitt Faculty Scholar in Energy Engineering Fund for providing funding that supported this work\.

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