# Desirable Characteristics for AI Teaching Assistants in Programming Education

arXiv: https://arxiv.org/abs/2405.14178 · HTML: https://mlsanity.com/paper/2405.14178/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2024.

## Built on by (most cited first, 17 of 17)

- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
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- [Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools](https://mlsanity.com/paper/2412.14732/) (2024)
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- [Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Education](https://mlsanity.com/paper/2502.20527/) (2025)
- [From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions](https://mlsanity.com/paper/2504.18691/) (2025)
- [Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails](https://mlsanity.com/paper/2504.11146/) (2025)
- [Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems using Large Multimodal Models](https://mlsanity.com/paper/2412.11088/) (2024)
- [Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Education](https://mlsanity.com/paper/2505.18220/) (2025)
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- ["Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbots](https://mlsanity.com/paper/2603.00057/) (2026)
- [Utilizing ChatGPT in a Data Structures and Algorithms Course: A Teaching Assistant's Perspective](https://mlsanity.com/paper/2410.08899/) (2024)
- ["All Roads Lead to ChatGPT": How Generative AI is Eroding Social Interactions and Student Learning Communities](https://mlsanity.com/paper/2504.09779/) (2025)
- [A Comparative Study of Student Perspectives on Technical Writing Feedback Quality: Evaluating LLMs, SLMs, and Humans in Computer Science Topics](https://mlsanity.com/paper/2601.11541/) (2025)
- [Learning by Teaching: Engaging Students as Instructors of Large Language Models in Computer Science Education](https://mlsanity.com/paper/2508.05979/) (2025)
- [From Domain Understanding to Design Readiness: a playbook for GenAI-supported learning in Software Engineering](https://mlsanity.com/paper/2604.00120/) (2026)
- [An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration](https://mlsanity.com/paper/2606.12425/) (2026)

## Abstract (arXiv)

Providing timely and personalized feedback to large numbers of students is a long-standing challenge in programming courses. Relying on human teaching assistants (TAs) has been extensively studied, revealing a number of potential shortcomings. These include inequitable access for students with low confidence when needing support, as well as situations where TAs provide direct solutions without helping students to develop their own problem-solving skills. With the advent of powerful large language models (LLMs), digital teaching assistants configured for programming contexts have emerged as an appealing and scalable way to provide instant, equitable, round-the-clock support. Although digital TAs can provide a variety of help for programming tasks, from high-level problem solving advice to direct solution generation, the effectiveness of such tools depends on their ability to promote meaningful learning experiences. If students find the guardrails implemented in digital TAs too constraining, or if other expectations are not met, they may seek assistance in ways that do not help them learn. Thus, it is essential to identify the features that students believe make digital teaching assistants valuable. We deployed an LLM-powered digital assistant in an introductory programming course and collected student feedback (n=813) on the characteristics of the tool they perceived to be most important. Our results highlight that students value such tools for their ability to provide instant, engaging support, particularly during peak times such as before assessment deadlines. They also expressed a strong preference for features that enable them to retain autonomy in their learning journey, such as scaffolding that helps to guide them through problem-solving steps rather than simply being shown direct solutions.