The Robots are Here: Navigating the Generative AI Revolution in Computing Education
arXiv:2310.00658 · doi:10.1145/3623762.3633499
Abstract
Recent advancements in artificial intelligence (AI) are fundamentally reshaping computing, with large language models (LLMs) now effectively being able to generate and interpret source code and natural language instructions. These emergent capabilities have sparked urgent questions in the computing education community around how educators should adapt their pedagogy to address the challenges and to leverage the opportunities presented by this new technology. In this working group report, we undertake a comprehensive exploration of LLMs in the context of computing education and make five significant contributions. First, we provide a detailed review of the literature on LLMs in computing education and synthesise findings from 71 primary articles. Second, we report the findings of a survey of computing students and instructors from across 20 countries, capturing prevailing attitudes towards LLMs and their use in computing education contexts. Third, to understand how pedagogy is already changing, we offer insights collected from in-depth interviews with 22 computing educators from five continents who have already adapted their curricula and assessments. Fourth, we use the ACM Code of Ethics to frame a discussion of ethical issues raised by the use of large language models in computing education, and we provide concrete advice for policy makers, educators, and students. Finally, we benchmark the performance of LLMs on various computing education datasets, and highlight the extent to which the capabilities of current models are rapidly improving. Our aim is that this report will serve as a focal point for both researchers and practitioners who are exploring, adapting, using, and evaluating LLMs and LLM-based tools in computing classrooms.
39 pages of content + 12 pages of references and appendices
References in corpus (15)
- Automatic Generation of Programming Exercises and Code Explanations using Large Language Models
- Successful Combination of Database Search and Snowballing for Identification of Primary Studies in Systematic Literature Studies
- Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming
- Co-Writing with Opinionated Language Models Affects Users' Views
- "It's Weird That it Knows What I Want": Usability and Interactions with Copilot for Novice Programmers
- Comparing Code Explanations Created by Students and Large Language Models
- Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests
- Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses
- AI-driven Development Is Here: Should You Worry?
- Exploring the Role of AI Assistants in Computer Science Education: Methods, Implications, and Instructor Perspectives
- Large Language Models in Introductory Programming Education: ChatGPT's Performance and Implications for Assessments
- Robosourcing Educational Resources -- Leveraging Large Language Models for Learnersourcing
- Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
- Generative AI in Computing Education: Perspectives of Students and Instructors
- Exploring the Potential of Large Language Models to Generate Formative Programming Feedback
Cited by in corpus (29)
- A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education
- Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Study
- Desirable Characteristics for AI Teaching Assistants in Programming Education
- Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant
- Feedback-Generation for Programming Exercises With GPT-4
- AI Literacy in K-12 and Higher Education in the Wake of Generative AI: An Integrative Review
- Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course
- Evaluating the Application of Large Language Models to Generate Feedback in Programming Education
- Early Adoption of Generative Artificial Intelligence in Computing Education: Emergent Student Use Cases and Perspectives in 2023
- How Do Programming Students Use Generative AI?
- Enhancing Programming Error Messages in Real Time with Generative AI
- Automating Autograding: Large Language Models as Test Suite Generators for Introductory Programming
- One Step at a Time: Combining LLMs and Static Analysis to Generate Next-Step Hints for Programming Tasks
- Leveraging Lecture Content for Improved Feedback: Explorations with GPT-4 and Retrieval Augmented Generation
- Automating Personalized Parsons Problems with Customized Contexts and Concepts
- Probing the Unknown: Exploring Student Interactions with Probeable Problems at Scale in Introductory Programming
- Did Alice Do Wrong? Cross-Cultural Differences in Student Perceptions of Generative AI Use in University Computing Education
- Unlimited Practice Opportunities: Automated Generation of Comprehensive, Personalized Programming Tasks
- GenAI Voice Mode in Programming Education
- A Systematic Literature Review of the Use of GenAI Assistants for Code Comprehension: Implications for Computing Education Research and Practice
- Excited, Skeptical, or Worried? A Multi-Institutional Study of Student Views on Generative AI in Computing Education
- PLAID: Supporting Computing Instructors to Identify Domain-Specific Programming Plans at Scale
- Student Engagement with GenAI's Tutoring Feedback: A Mixed Methods Study
- Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems
- From Toil to Thought: Designing for Strategic Exploration and Responsible AI in Systematic Literature Reviews
- Conversations over Clicks: Impact of Chatbots on Information Search in Interdisciplinary Learning
- Self-Regulated Personal Contracts as a Harm Reduction Approach to Generative AI in Undergraduate Programming Education
- Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants
- A Comparative Study of Student Perspectives on Technical Writing Feedback Quality: Evaluating LLMs, SLMs, and Humans in Computer Science Topics