mlsanityData to 14 Oct 2026

"It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers

arXiv 2304.02491 ↗First posted 5 Apr 2023cs.HC cs.AIcited by 64

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Abstract (arXiv)

Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy. This is likely to have profound effects in the classroom, where novices learning to code can now use free tools to automatically suggest solutions to programming exercises and assignments. However, little is currently known about how novices interact with these tools in practice. We present the first study that observes students at the introductory level using one such code auto-generating tool, Github Copilot, on a typical introductory programming (CS1) assignment. Through observations and interviews we explore student perceptions of the benefits and pitfalls of this technology for learning, present new observed interaction patterns, and discuss cognitive and metacognitive difficulties faced by students. We consider design implications of these findings, specifically in terms of how tools like Copilot can better support and scaffold the novice programming experience.

Key facts. Cited by 64 papers in the map (data through 14 Oct 2026). Builds on 26 of its 122 references that are in the map. First posted on arXiv 5 Apr 2023.

Builds on

26 of 122 references.

References · 26

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  4. 2021Program Synthesis with Large Language Modelscited by 3,637
  5. 2022Emergent Abilities of Large Language Modelscited by 2,512
  6. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
  7. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  8. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  9. 2022Galactica: A Large Language Model for Sciencecited by 744
  10. 2021Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributionscited by 431
  11. 2022CodeT: Code Generation with Generated Testscited by 384
  12. 2023The Impact of AI on Developer Productivity: Evidence from GitHub Copilotcited by 274
14 more
  1. 2020Questioning the AI: Informing Design Practices for Explainable AI User Experiencescited by 253
  2. 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 218
  3. 2022Automatic Generation of Programming Exercises and Code Explanations using Large Language Modelscited by 126
  4. 2021Examining Zero-Shot Vulnerability Repair with Large Language Modelscited by 105
  5. 2022Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generationcited by 99
  6. 2021A Neural Network Solves, Explains, and Generates University Math Problems by Program Synthesis and Few-Shot Learning at Human Levelcited by 96
  7. 2022Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Bookcited by 84
  8. 2022Using Large Language Models to Enhance Programming Error Messagescited by 67
  9. 2021Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactionscited by 35
  10. 2022AI-driven Development Is Here: Should You Worry?cited by 15
  11. 2022Pop Quiz! Can a Large Language Model Help With Reverse Engineering?cited by 14
  12. 2019A Survey of Automated Programming Hint Generation -- The HINTS Frameworkcited by 12
  13. 2022Fooling MOSS Detection with Pretrained Language Modelscited by 12
  14. 2021The Role of Working Memory in Program Tracingcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 12; 2024: 23; 2025: 18; 2026: 11.

64 papers.

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  1. 2023The Robots are Here: Navigating the Generative AI Revolution in Computing Educationcited by 86
  2. 2024CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needscited by 84
  3. 2023CodeHelp: Using Large Language Models with Guardrails for Scalable Support in Programming Classescited by 72
  4. 2023Computing Education in the Era of Generative AIcited by 52
  5. 2024The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmerscited by 48
  6. 2023Exploring the Responses of Large Language Models to Beginner Programmers' Help Requestscited by 46
  7. 2024Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forwardcited by 45
  8. 2023Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Coursescited by 39
  9. 2023StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Codecited by 32
  10. 2024The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmerscited by 30
  11. 2023Exploring the Potential of Large Language Models to Generate Formative Programming Feedbackcited by 28
  12. 2024An Empirical Study on Usage and Perceptions of LLMs in a Software Engineering Projectcited by 28
38 more
  1. 2024What Should We Engineer in Prompts? Training Humans in Requirement-Driven LLM Usecited by 24
  2. 2023Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programmingcited by 20
  3. 2023Exploring and Characterizing Large Language Models For Embedded System Development and Debuggingcited by 20
  4. 2023Next-Step Hint Generation for Introductory Programming Using Large Language Modelscited by 20
  5. 2023Promptly: Using Prompt Problems to Teach Learners How to Effectively Utilize AI Code Generatorscited by 17
  6. 2025CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Codingcited by 12
  7. 2024How Novice Programmers Use and Experience ChatGPT when Solving Programming Exercises in an Introductory Coursecited by 11
  8. 2023Large Language Models in Introductory Programming Education: ChatGPT's Performance and Implications for Assessmentscited by 10
  9. 2024Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factorscited by 10
  10. 2024Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistantscited by 10
  11. 2025How Do Programming Students Use Generative AI?cited by 10
  12. 2024Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Coursecited by 9
  13. 2024Analyzing Chat Protocols of Novice Programmers Solving Introductory Programming Tasks with ChatGPTcited by 8
  14. 2024"ChatGPT Is Here to Help, Not to Replace Anybody" -- An Evaluation of Students' Opinions On Integrating ChatGPT In CS Coursescited by 6
  15. 2024Students' Perceptions and Use of Generative AI Tools for Programming Across Different Computing Coursescited by 6
  16. 2026From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interactioncited by 6
  17. 2024A Transformer-Based Approach for Smart Invocation of Automatic Code Completioncited by 5
  18. 2024AIGCodeSet: A New Annotated Dataset for AI Generated Code Detectioncited by 5
  19. 2025TableTalk: Scaffolding Spreadsheet Development with a Language Agentcited by 5
  20. 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
  21. 2024Exploring Human-LLM Conversations: Mental Models and the Originator of Toxicitycited by 4
  22. 2024Survey of User Interface Design and Interaction Techniques in Generative AI Applicationscited by 4
  23. 2025From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactionscited by 4
  24. 2025Understanding Human-AI Trust in Educationcited by 4
  25. 2025"My productivity is boosted, but ..." Demystifying Users' Perception on AI Coding Assistantscited by 4
  26. 2024Enhancing Programming Error Messages in Real Time with Generative AIcited by 3
  27. 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Reviewcited by 3
  28. 2025Mental Models of Autonomy and Sentience Shape Reactions to AIcited by 3
  29. 2026Are We All Using Agents the Same Way? An Empirical Study of Core and Peripheral Developers Use of Coding Agentscited by 3
  30. 2024Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysiscited by 2
  31. 2024Not the Silver Bullet: LLM-enhanced Programming Error Messages are Ineffective in Practicecited by 2
  32. 2024"Give me the code" -- Log Analysis of First-Year CS Students' Interactions With GPTcited by 2
  33. 2025Navigating Pitfalls: Evaluating LLMs in Machine Learning Programming Educationcited by 2
  34. 2025Do AI models help produce verified bug fixes?cited by 2
  35. 2025MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarioscited by 2
  36. 2025Coding With AI: From a Reflection on Industrial Practices to Future Computer Science and Software Engineering Educationcited by 2
  37. 2026What Drives Students' Use of AI Chatbots? Technology Acceptance in Conversational AIcited by 2
  38. 2024Pensieve Discuss: Scalable Small-Group CS Tutoring System with AIcited by 1

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