# Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning arXiv: https://arxiv.org/abs/2601.12134 ยท HTML: https://mlsanity.com/paper/2601.12134/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 129 references that are in the map. - First posted on arXiv 17 Jan 2026. ## Builds on (most cited first, 30 of 34) - [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025) - [Automatic Generation of Programming Exercises and Code Explanations using Large Language Models](https://mlsanity.com/paper/2206.11861/) (2022) - [Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task](https://mlsanity.com/paper/2506.08872/) (2025) - [Programming Is Hard -- Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation](https://mlsanity.com/paper/2212.01020/) (2022) - [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023) - [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024) - [Beware of Metacognitive Laziness: Effects of Generative Artificial Intelligence on Learning Motivation, Processes, and Performance](https://mlsanity.com/paper/2412.09315/) (2024) - [VISAR: A Human-AI Argumentative Writing Assistant with Visual Programming and Rapid Draft Prototyping](https://mlsanity.com/paper/2304.07810/) (2023) - [Need Help? Designing Proactive AI Assistants for Programming](https://mlsanity.com/paper/2410.04596/) (2024) - [Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors](https://mlsanity.com/paper/2306.17156/) (2023) - [Teach AI How to Code: Using Large Language Models as Teachable Agents for Programming Education](https://mlsanity.com/paper/2309.14534/) (2023) - [Assistance or Disruption? Exploring and Evaluating the Design and Trade-offs of Proactive AI Programming Support](https://mlsanity.com/paper/2502.18658/) (2025) - [How to Teach Programming in the AI Era? Using LLMs as a Teachable Agent for Debugging](https://mlsanity.com/paper/2310.05292/) (2023) - [Are Large Language Models Reliable Argument Quality Annotators?](https://mlsanity.com/paper/2404.09696/) (2024) - [Is AI the better programming partner? Human-Human Pair Programming vs. Human-AI pAIr Programming](https://mlsanity.com/paper/2306.05153/) (2023) - [Next-Step Hint Generation for Introductory Programming Using Large Language Models](https://mlsanity.com/paper/2312.10055/) (2023) - [CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models](https://mlsanity.com/paper/2402.15265/) (2024) - [Examining the Use and Impact of an AI Code Assistant on Developer Productivity and Experience in the Enterprise](https://mlsanity.com/paper/2412.06603/) (2024) - [CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming](https://mlsanity.com/paper/2310.09235/) (2023) - [Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems](https://mlsanity.com/paper/2209.05948/) (2022) - [Applying General Turn-taking Models to Conversational Human-Robot Interaction](https://mlsanity.com/paper/2501.08946/) (2025) - [Exploring the Role of AI Assistants in Computer Science Education: Methods, Implications, and Instructor Perspectives](https://mlsanity.com/paper/2306.03289/) (2023) - [Controlling AI Agent Participation in Group Conversations: A Human-Centered Approach](https://mlsanity.com/paper/2501.17258/) (2025) - [CodingGenie: A Proactive LLM-Powered Programming Assistant](https://mlsanity.com/paper/2503.14724/) (2025) - [Understanding User Perceptions, Collaborative Experience and User Engagement in Different Human-AI Interaction Designs for Co-Creative Systems](https://mlsanity.com/paper/2204.13217/) (2022) - [CodeA11y: Making AI Coding Assistants Useful for Accessible Web Development](https://mlsanity.com/paper/2502.10884/) (2025) - [LADICA: A Large Shared Display Interface for Generative AI Cognitive Assistance in Co-Located Team Collaboration](https://mlsanity.com/paper/2409.13968/) (2024) - [Conversate: Supporting Reflective Learning in Interview Practice Through Interactive Simulation and Dialogic Feedback](https://mlsanity.com/paper/2410.05570/) (2024) - [Understanding User Mental Models in AI-Driven Code Completion Tools: Insights from an Elicitation Study](https://mlsanity.com/paper/2502.02194/) (2025) - [How Managers Perceive AI-Assisted Conversational Training for Workplace Communication](https://mlsanity.com/paper/2505.14452/) (2025) ## Built on by (most cited first, 1 of 1) - [LLMs are General Asynchronous Agents](https://mlsanity.com/paper/2609.35427/) (2026) ## Abstract (arXiv) As AI assistance becomes embedded in programming practice, researchers have increasingly examined how these systems help learners generate code and work more efficiently. However, these studies often position AI as a replacement for human collaboration and overlook the social and learning-oriented aspects that emerge in collaborative programming. Our work introduces human-human-AI (HHAI) triadic programming, where an AI agent serves as an additional collaborator rather than a substitute for a human partner. Through a within-subjects study with 20 participants, we show that triadic collaboration enhances collaborative learning and social presence compared to the dyadic human-AI (HAI) baseline. In the triadic HHAI conditions, participants relied significantly less on AI-generated code in their work. This effect was strongest in the HHAI-shared condition, where participants had an increased sense of responsibility to understand AI suggestions before applying them. These findings demonstrate how triadic settings activate socially shared regulation of learning by making AI use visible and accountable to a human peer, suggesting that AI systems that augment rather than automate peer collaboration can better preserve the learning processes that collaborative programming relies on.