A collection of interesting papers about how well generative AI works in different higher education settings.
Because we're starting to get to the point where we can codify best practice in using generative AI in the university. This linkpost is for anyone looking to understand what generative AI can and can't do in the classroom, and where it's most useful.
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- ChatGPT for good? On opportunities and challenges of large language models for education
- Instructors as Innovators: a Future-focused Approach to New AI Learning Opportunities, With Prompts
- Using Large Language Models to Automatically Identify Programming Concepts in Code Snippets
- ChatGPT's capabilities in providing feedback on undergraduate students’ argumentation: A case study
- The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Development
- Towards Open Natural Language Feedback Generation for Novice Programmers using Large Language Models
- Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book
- Programming Is Hard - Or at Least It Used to Be: Educational Opportunities and Challenges of AI Code Generation
- “What It Wants Me To Say”: Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
- Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models
- Exploring the Responses of Large Language Models to Beginner Programmers' Help Requests
- The Implications of Large Language Models for CS Teachers and Students
- StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Code
- Computing Education in the Era of Generative AI
- Comparing Code Explanations Created by Students and Large Language Models
- Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review
- Prompts First, Finally
- CourseAssist: Pedagogically Appropriate Question Answering System for Computer Science Education
- Generative AI Can Harm Learning
- Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines
- Leveraging Large Language Models for Actionable Course Evaluation Student Feedback to Lecturers
- Exploring Generative AI Policies in Higher Education: A Comparative Perspective from China, Japan, Mongolia, and the USA
- Evaluating Large Language Models Trained On Code
- Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models
- Language Models Can Teach Themselves to Program Better
- Using Large Language Models to Generate JUnit Tests: An Empirical Study
- Code Generation Tools (Almost) for Free? A Study of Few-Shot, Pre-Trained Language Models on Code
- Large Language Models and Simple, Stupid Bugs
- On the Rise and Fall of Simple Stupid Bugs: a Life-Cycle Analysis of SStuBs
- Ten Hard Problems in Artificial Intelligence We Must Get Right
- Seeing Like an AI: How LLMs Apply (and Misapply) Wikipedia Neutrality Norms
- Unraveling overoptimism and publication bias in ML-driven science
- A False Sense of Safety: Unsafe Information Leakage in 'Safe' AI Responses
- On the Worst Prompt Performance of Large Language Models
- AI AI Bias: Large Language Models Favor Their Own Generated Content
- Using Artificial Intelligence to Accelerate Collective Intelligence: Policy Synth and Smarter Crowdsourcing
- LLMs left, right, and center: Assessing GPT's capabilities to label political bias from web domains
- Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges
- Watching the Generative AI Hype Bubble Deflect
- Techniques for supercharging academic writing with generative AI