# A Comparative Study of Student Perspectives on Technical Writing Feedback Quality: Evaluating LLMs, SLMs, and Humans in Computer Science Topics

arXiv: https://arxiv.org/abs/2601.11541 · HTML: https://mlsanity.com/paper/2601.11541/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation.
- Builds on 10 of its 37 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Placed in

- LLM alignment / RLHF / preference optimisation / [Human values and AI feedback alignment](https://mlsanity.com/alignment/human-values-and-ai-feedback-alignment/)

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [Students' Voices on Generative AI: Perceptions, Benefits, and Challenges in Higher Education](https://mlsanity.com/paper/2305.00290/) (2023)
- [Impact of Guidance and Interaction Strategies for LLM Use on Learner Performance and Perception](https://mlsanity.com/paper/2310.13712/) (2023)
- [Desirable Characteristics for AI Teaching Assistants in Programming Education](https://mlsanity.com/paper/2405.14178/) (2024)
- [Automated Feedback Generation for Competition-Level Code](https://mlsanity.com/paper/2206.01848/) (2022)
- [Impact of combining human and analytics feedback on students' engagement with, and performance in, reflective writing tasks](https://mlsanity.com/paper/2211.08222/) (2022)
- [Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback](https://mlsanity.com/paper/2509.10647/) (2025)

## Built on by (most cited first, 1 of 1)

- [FOXGLOVE: Understanding Goal-Oriented and Anchored Writing Feedback from Experts and LLMs on Argumentative Essays](https://mlsanity.com/paper/2606.06271/) (2026)

## Abstract (arXiv)

To address the scalability of feedback in computer science while mitigating the privacy and cost limitations of commercial Large Language Models (LLMs), this study evaluates a locally hosted Small Language Model (SLM). We deployed a quantized Llama-3.1, GPT-4, and human instructors across introductory programming (N=176), operating systems (N=80), and a writing seminar (N=7). Mixed-methods analysis of student perceptions reveals that while the local SLM matched commercial LLMs and was rated higher by students for readability and actionability in technical courses, human feedback remained more favoured for highly specialized writing tasks. We demonstrate that local SLMs offer a privacy-preserving, zero-marginal-cost alternative for foundational feedback, supporting a tiered pedagogical framework where AI handles structural guidance while instructors focus on high-level conceptual scaffolding.