# Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools

arXiv: https://arxiv.org/abs/2412.14732 · HTML: https://mlsanity.com/paper/2412.14732/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 64 of its 226 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 30 of 64)

- [Using an LLM to Help With Code Understanding](https://mlsanity.com/paper/2307.08177/) (2023)
- [Grounded Copilot: How Programmers Interact with Code-Generating Models](https://mlsanity.com/paper/2206.15000/) (2022)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022)
- [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022)
- [Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming](https://mlsanity.com/paper/2210.14306/) (2022)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [The Metacognitive Demands and Opportunities of Generative AI](https://mlsanity.com/paper/2312.10893/) (2023)
- [The Robots are Here: Navigating the Generative AI Revolution in Computing Education](https://mlsanity.com/paper/2310.00658/) (2023)
- [Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book](https://mlsanity.com/paper/2211.02265/) (2022)
- [CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs](https://mlsanity.com/paper/2401.11314/) (2024)
- [A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challenges](https://mlsanity.com/paper/2303.17125/) (2023)
- [CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classes](https://mlsanity.com/paper/2308.06921/) (2023)
- [Using Large Language Models to Enhance Programming Error Messages](https://mlsanity.com/paper/2210.11630/) (2022)
- [Comparing Code Explanations Created by Students and Large Language Models](https://mlsanity.com/paper/2304.03938/) (2023)
- [Computing Education in the Era of Generative AI](https://mlsanity.com/paper/2306.02608/) (2023)
- [The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers](https://mlsanity.com/paper/2405.17739/) (2024)
- [Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses](https://mlsanity.com/paper/2306.10073/) (2023)
- [Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education](https://mlsanity.com/paper/2309.14534/) (2023)
- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)
- [Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?](https://mlsanity.com/paper/2306.13906/) (2023)
- [Prompt Problems: A New Programming Exercise for the Generative AI Era](https://mlsanity.com/paper/2311.05943/) (2023)
- [How Beginning Programmers and Code LLMs (Mis)read Each Other](https://mlsanity.com/paper/2401.15232/) (2024)
- [Exploring the Potential of Large Language Models to Generate Formative Programming Feedback](https://mlsanity.com/paper/2309.00029/) (2023)
- [The Effects of Generative AI on Computing Students' Help-Seeking Preferences](https://mlsanity.com/paper/2401.02262/) (2024)
- [Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chat](https://mlsanity.com/paper/2401.17163/) (2024)
- [Next-Step Hint Generation for Introductory Programming Using Large Language Models](https://mlsanity.com/paper/2312.10055/) (2023)
- [Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generators](https://mlsanity.com/paper/2307.16364/) (2023)
- [Desirable Characteristics for AI Teaching Assistants in Programming Education](https://mlsanity.com/paper/2405.14178/) (2024)
- [Trust in Generative AI among students: An Exploratory Study](https://mlsanity.com/paper/2310.04631/) (2023)

## Built on by (most cited first, 14 of 14)

- [The Future of Generative AI in Software Engineering: A Vision from Industry and Academia in the European GENIUS Project](https://mlsanity.com/paper/2511.01348/) (2025)
- [Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review](https://mlsanity.com/paper/2503.07556/) (2025)
- [Exploring Student Behaviors and Motivations using AI TAs with Optional Guardrails](https://mlsanity.com/paper/2504.11146/) (2025)
- ["Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbots](https://mlsanity.com/paper/2603.00057/) (2026)
- ["All Roads Lead to ChatGPT": How Generative AI is Eroding Social Interactions and Student Learning Communities](https://mlsanity.com/paper/2504.09779/) (2025)
- [Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools](https://mlsanity.com/paper/2507.05305/) (2025)
- [Students' Feedback Requests and Interactions with the SCRIPT Chatbot: Do They Get What They Ask For?](https://mlsanity.com/paper/2507.17258/) (2025)
- [Demystify, Use, Reflect: Preparing students to be informed LLM-users](https://mlsanity.com/paper/2511.11764/) (2025)
- [Beyond the Desk: Barriers and Future Opportunities for AI to Assist Scientists in Embodied Physical Tasks](https://mlsanity.com/paper/2603.19504/) (2026)
- [GenAI Voice Mode in Programming Education](https://mlsanity.com/paper/2509.10596/) (2025)
- [AI-Generated Slides: Are They Good? Can Students Tell?](https://mlsanity.com/paper/2605.13532/) (2026)
- [To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checks](https://mlsanity.com/paper/2606.30549/) (2026)
- [Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Course](https://mlsanity.com/paper/2607.24755/) (2026)
- [Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026](https://mlsanity.com/paper/2608.16318/) (2026)

## Abstract (arXiv)

Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community.