mlsanityData to 7 Oct 2026

The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers

arXiv 2405.17739 ↗First posted 28 May 2024cs.AI cs.HCcited by 46

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Abstract (arXiv)

Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounter multiple metacognitive difficulties while programming. Novices are typically unaware of how these difficulties are hindering their progress. Meanwhile, many novices are now programming with generative AI (GenAI), which can provide complete solutions to most introductory programming problems, code suggestions, hints for next steps when stuck, and explain cryptic error messages. Its impact on novice metacognition has only started to be explored. Here we replicate a previous study that examined novice programming problem solving behavior and extend it by incorporating GenAI tools. Through 21 lab sessions consisting of participant observation, interview, and eye tracking, we explore how novices are coding with GenAI tools. Although 20 of 21 students completed the assigned programming problem, our findings show an unfortunate divide in the use of GenAI tools between students who accelerated and students who struggled. Students who accelerated were able to use GenAI to create code they already intended to make and were able to ignore unhelpful or incorrect inline code suggestions. But for students who struggled, our findings indicate that previously known metacognitive difficulties persist, and that GenAI unfortunately can compound them and even introduce new metacognitive difficulties. Furthermore, struggling students often expressed cognitive dissonance about their problem solving ability, thought they performed better than they did, and finished with an illusion of competence. Based on our observations from both groups, we propose ways to scaffold the novice GenAI experience and make suggestions for future work.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Builds on 24 of its 76 references that are in the map. First posted on arXiv 28 May 2024.

Builds on

24 of 76 references.

References · 24

  1. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 264
  2. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 216
  3. 2022Automatic Generation of Programming Exercises and Code Explanations using Large Language Modelscited by 124
  4. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 97
  5. 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
  6. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 85
  7. 2023The Metacognitive Demands and Opportunities of Generative AIcited by 85
  8. 2022Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Bookcited by 84
  9. 2024CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needscited by 81
  10. 2023CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classescited by 71
  11. 2022Using Large Language Models to Enhance Programming Error Messagescited by 67
  12. 2023Comparing Code Explanations Created by Students and Large Language Modelscited by 63
12 more
  1. 2023"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmerscited by 62
  2. 2023Computing Education in the Era of Generative AIcited by 50
  3. 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requestscited by 44
  4. 2023Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Coursescited by 38
  5. 2023Prompt Problems: A New Programming Exercise for the Generative AI Eracited by 31
  6. 2024The Effects of Generative AI on Computing Students' Help-Seeking Preferencescited by 22
  7. 2023Generative AI in Computing Education: Perspectives of Students and Instructorscited by 17
  8. 2024Desirable Characteristics for AI Teaching Assistants in Programming Educationcited by 16
  9. 2024CS1-LLM: Integrating LLMs into CS1 Instructioncited by 15
  10. 2024Ivie: Lightweight Anchored Explanations of Just-Generated Codecited by 14
  11. 2023More Robots are Coming: Large Multimodal Models (ChatGPT) can Solve Visually Diverse Images of Parsons Problemscited by 10
  12. 2024"ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Coursescited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 18; 2026: 18.

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  1. 2024Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learningcited by 16
  2. 2024Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Toolscited by 13
  3. 2025Leveraging Dual Process Theory in Language Agent Framework for Real-time Simultaneous Human-AI Collaborationcited by 10
  4. 2025How Do Programming Students Use Generative AI?cited by 9
  5. 2024Beyond Text-to-Text: An Overview of Multimodal and Generative Artificial Intelligence for Education Using Topic Modelingcited by 6
  6. 2024Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Coursescited by 5
  7. 2025Generative AI in Education: From Foundational Insights to the Socratic Playground for Learningcited by 5
  8. 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
  9. 2024The Evolving Usage of GenAI by Computing Studentscited by 4
  10. 2025From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactionscited by 4
  11. 2025More Than a Score: Probing the Impact of Prompt Specificity on LLM Code Generationcited by 4
  12. 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Reviewcited by 3
34 more
  1. 2026Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agentscited by 3
  2. 2024Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learnerscited by 2
  3. 2025Supervised Fine-Tuning LLMs to Behave as Pedagogical Agents in Programming Educationcited by 2
  4. 2025Improving Student-AI Interaction Through Pedagogical Prompting: An Example in Computer Science Educationcited by 2
  5. 2025Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Educationcited by 2
  6. 2026"Bespoke Bots": Diverse Instructor Needs for Customizing Generative AI Classroom Chatbotscited by 2
  7. 2026Unpacking Vibe Coding: Help-Seeking Processes in Student-AI Interactions While Programmingcited by 2
  8. 2024Integrating Natural Language Prompting Tasks in Introductory Programming Coursescited by 1
  9. 2024Seeing the Forest and the Trees: Solving Visual Graph and Tree Based Data Structure Problems using Large Multimodal Modelscited by 1
  10. 2025Prompt Programming: A Platform for Dialogue-based Computational Problem Solving with Generative AI Modelscited by 1
  11. 2025"All Roads Lead to ChatGPT": How Generative AI is Eroding Social Interactions and Student Learning Communitiescited by 1
  12. 2025User Misconceptions of LLM-Based Conversational Programming Assistantscited by 1
  13. 2024LLMs Integration in Software Engineering Team Projects: Roles, Impact, and a Pedagogical Design Space for AI Tools in Computing Educationcited by 0
  14. 2024Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Educationcited by 0
  15. 2025To Google or To ChatGPT? A Comparison of CS2 Students' Information Gathering Approaches and Outcomescited by 0
  16. 2025Can AI support student engagement in classroom activities in higher education?cited by 0
  17. 2025GenAI Voice Mode in Programming Educationcited by 0
  18. 2025Feasibility of AI-Assisted Programming for End-User Developmentcited by 0
  19. 2025From Pilots to Practices: A Scoping Review of GenAI-Enabled Personalization in Computer Science Educationcited by 0
  20. 2026Agentic Education: Using Claude Code to Teach Claude Codecited by 0
  21. 2026AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domainscited by 0
  22. 2026A paradox of AI fluencycited by 0
  23. 2026The Pedagogy of AI Mistakes: Fostering Higher-Order Thinkingcited by 0
  24. 2026Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utilitycited by 0
  25. 2026Leveraging BART to Assess CS1 C++ Programming Assignments using Rubric-based Criteriacited by 0
  26. 2026AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspectivecited by 0
  27. 2026To Tab or Not to Tab: Measuring Critical Engagement in AI Code Completion Tools Using Behavioral Signals and Attention Checkscited by 0
  28. 2026From Execution to Education: A Bloom-Aligned Framework for Measuring Educational Control in LLMscited by 0
  29. 2026Patterns of Learner-AI Interaction and Academic Performance in an Object-Oriented Programming Coursecited by 0
  30. 2026From Idea to Classroom in Days: Using "Vibe Coding" to Create a Programming Process Visualizer from IDE Activity Logscited by 0
  31. 2026Is Solving Better Than Evaluating GenAI Solutions?cited by 0
  32. 2026Educating the Agentic Engineer: Curricula, Collaboration, and Continuous Learning in the AI Eracited by 0
  33. 2026Principal Trait Analysis: Towards Deriving "Skills" in Human-AI Collaborationcited by 0
  34. 2026Ownership in AI-Assisted Everyday Taskscited by 0