mlsanityData to 7 Oct 2026

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

arXiv 2412.14732 ↗First posted 19 Dec 2024cs.CY cs.AI cs.HCcited by 13

Not on the map. Why?

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 64 of its 226 references that are in the map. First posted on arXiv 19 Dec 2024.

Builds on

64 of 226 references.

References · 64

  1. 2023Using an LLM to Help With Code Understandingcited by 222
  2. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  3. 2022Do Users Write More Insecure Code with AI Assistants?cited by 186
  4. 2022Automatic Generation of Programming Exercises and Code Explanations using Large Language Modelscited by 124
  5. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 97
  6. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
  7. 2023Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programmingcited by 87
  8. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  9. 2023The Metacognitive Demands and Opportunities of Generative AIcited by 85
  10. 2022Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Bookcited by 84
  11. 2024CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needscited by 81
  12. 2023A Large-Scale Survey on the Usability of AI Programming Assistants: Successes and Challengescited by 75
38 more
  1. 2023CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classescited by 71
  2. 2022Using Large Language Models to Enhance Programming Error Messagescited by 67
  3. 2023Comparing Code Explanations Created by Students and Large Language Modelscited by 63
  4. 2023Computing Education in the Era of Generative AIcited by 50
  5. 2024The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmerscited by 46
  6. 2023Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Coursescited by 38
  7. 2023Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Educationcited by 38
  8. 2023A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Educationcited by 34
  9. 2023Prompt Problems: A New Programming Exercise for the Generative AI Eracited by 31
  10. 2024How Beginning Programmers and Code LLMs (Mis)read Each Othercited by 31
  11. 2023Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise?cited by 30
  12. 2023Exploring the Potential of Large Language Models to Generate Formative Programming Feedbackcited by 28
  13. 2024Learning Agent-based Modeling with LLM Companions: Experiences of Novices and Experts Using ChatGPT & NetLogo Chatcited by 23
  14. 2024The Effects of Generative AI on Computing Students' Help-Seeking Preferencescited by 22
  15. 2023Next-Step Hint Generation for Introductory Programming Using Large Language Modelscited by 20
  16. 2023Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generatorscited by 16
  17. 2023Trust in Generative AI among students: An Exploratory Studycited by 16
  18. 2024Desirable Characteristics for AI Teaching Assistants in Programming Educationcited by 16
  19. 2022Better Together? An Evaluation of AI-Supported Code Translationcited by 15
  20. 2024CS1-LLM: Integrating LLMs into CS1 Instructioncited by 15
  21. 2024Feedback-Generation for Programming Exercises With GPT-4cited by 14
  22. 2024The Role of Generative AI in Software Development Productivity: A Pilot Case Studycited by 14
  23. 2023Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistantcited by 13
  24. 2024Evaluating Contextually Personalized Programming Exercises Created with Generative AIcited by 13
  25. 2023The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and Millennial Generation teachers?cited by 12
  26. 2023Can We Trust AI-Generated Educational Content? Comparative Analysis of Human and AI-Generated Learning Resourcescited by 12
  27. 2023Dcc --help: Generating Context-Aware Compiler Error Explanations with Large Language Modelscited by 11
  28. 2024CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programmingcited by 11
  29. 2024Explaining Code with a Purpose: An Integrated Approach for Developing Code Comprehension and Prompting Skillscited by 11
  30. 2024How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Coursecited by 11
  31. 2023More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problemscited by 10
  32. 2024Evaluating the Application of Large Language Models to Generate Feedback in Programming Educationcited by 10
  33. 2024Exploring How Multiple Levels of GPT-Generated Programming Hints Support or Disappoint Novicescited by 10
  34. 2022Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcingcited by 9
  35. 2023From Large Language Models to Databases and Back: A discussion on research and educationcited by 9
  36. 2023How Far Are We? The Triumphs and Trials of Generative AI in Learning Software Engineeringcited by 9
  37. 2023AI-enhanced Auto-correction of Programming Exercises: How Effective is GPT-3.5?cited by 8
  38. 2024Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPTcited by 8

50 most cited of 64 All 64 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 6.

13 papers.

Sort: