# The Responsible Development of Automated Student Feedback with Generative AI

arXiv: https://arxiv.org/abs/2308.15334 · HTML: https://mlsanity.com/paper/2308.15334/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Benchmarking Foundation Models with Language-Model-as-an-Examiner](https://mlsanity.com/paper/2306.04181/) (2023)
- [Locally Typical Sampling](https://mlsanity.com/paper/2202.00666/) (2022)
- [Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests](https://mlsanity.com/paper/2306.05715/) (2023)
- [The Turing Trap: The Promise & Peril of Human-Like Artificial Intelligence](https://mlsanity.com/paper/2201.04200/) (2022)
- [Large Language Models (GPT) for automating feedback on programming assignments](https://mlsanity.com/paper/2307.00150/) (2023)
- [Superhuman Artificial Intelligence Can Improve Human Decision Making by Increasing Novelty](https://mlsanity.com/paper/2303.07462/) (2023)

## Built on by (most cited first, 3 of 3)

- [PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs](https://mlsanity.com/paper/2605.01123/) (2026)
- [SEFL: A Framework for Generating Synthetic Educational Assignment Feedback with LLM Agents](https://mlsanity.com/paper/2502.12927/) (2025)
- [BiST: A Gold Standard Bangla-English Bilingual Corpus for Sentence Structure and Tense Classification with Inter-Annotator Agreement](https://mlsanity.com/paper/2604.04708/) (2026)

## Abstract (arXiv)

Providing rich, constructive feedback to students is essential for supporting and enhancing their learning. Recent advancements in Generative Artificial Intelligence (AI), particularly with large language models (LLMs), present new opportunities to deliver scalable, repeatable, and instant feedback, effectively making abundant a resource that has historically been scarce and costly. From a technical perspective, this approach is now feasible due to breakthroughs in AI and Natural Language Processing (NLP). While the potential educational benefits are compelling, implementing these technologies also introduces a host of ethical considerations that must be thoughtfully addressed. One of the core advantages of AI systems is their ability to automate routine and mundane tasks, potentially freeing up human educators for more nuanced work. However, the ease of automation risks a ``tyranny of the majority'', where the diverse needs of minority or unique learners are overlooked, as they may be harder to systematize and less straightforward to accommodate. Ensuring inclusivity and equity in AI-generated feedback, therefore, becomes a critical aspect of responsible AI implementation in education. The process of developing machine learning models that produce valuable, personalized, and authentic feedback also requires significant input from human domain experts. Decisions around whose expertise is incorporated, how it is captured, and when it is applied have profound implications for the relevance and quality of the resulting feedback. Additionally, the maintenance and continuous refinement of these models are necessary to adapt feedback to evolving contextual, theoretical, and student-related factors. Without ongoing adaptation, feedback risks becoming obsolete or mismatched with the current needs of diverse student populations [...]