human-ai-collaboration
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- PaperJournal of Second Language Writing30 Jun 2026
Measuring self-regulation in student-GenAI collaborative revision of second language writing: Scale development and validation
Ting Zhao, Yan Ding, Zhongbin Hu, Limin Su et al.
The study develops and validates a scale for measuring self-regulation in second language writing when students collaboratively revise with generative AI. It focuses on the metacognitive and regulatory processes involved in human-AI interaction during revision.
- PaperarXiv — AI in Education (cs.CY)28 Jun 2026
LLMography: Transforming Human-AI Conversations into Traceability, Oversight, and Auditability Indicators
Mohammed Bousmah
LLMography proposes a framework to document and evaluate human-AI conversation traces, generating KPIs like Prompt Quality and Human Direction scores. A pilot study with engineering students found most interactions were human-directed co-production. The framework shifts AI transparency from output detection to interaction history.
Original abstract
The growing use of Large Language Models (LLMs) in education, software engineering, academic writing, and technical documentation raises a key question: how can we evaluate not only AI-assisted outputs, but also the interaction process that produced them? Current debates often focus on detecting whether a final artifact was generated by AI, while overlooking the conversation history that reveals human direction, AI contribution, corrections, validation, and traceability. This paper introduces LLMography, a framework for transforming Human-AI conversations into measurable indicators of provenance, human contribution, AI dependency, reproducibility, and auditability. By analogy with bibliography and webography, LLMography documents the dynamic trajectory of interaction between a human and a Large Language Model as a structured trace of Human-AI co-production. We present a prototype that analyzes Human-AI conversation traces and generates KPI reports including Prompt Quality Score, Human Direction Score, AI Dependency Level, Auditability Score, Final Output Traceability, Privacy Risk Level, and a recommended LLMography label. A preliminary exploratory evaluation was conducted on 19 anonymized audit reports from engineering students. Most interactions were classified as Human-AI co-produced, with average scores of 86.8/100 for Human Direction, 81.9/100 for Prompt Quality, 72.8/100 for Auditability, and 77.1/100 for Final Output Traceability. The paper also applies LLMography to its own writing process, classified as human-originated, human-directed, AI-assisted co-production. The findings suggest that AI transparency should move beyond output detection toward documenting the history of interaction.
- PaperComputers & Education23 Jun 2026
Equipping elementary school students with self-regulated learning through human-AI collaboration in online learning
Xinyi Luo, Sikai Wang, Khe Foon Hew
This study explores how human-AI collaboration can support elementary school students in developing self-regulated learning skills in online environments. It examines the design and implementation of AI tools that work alongside teachers and students to foster autonomy and metacognition.
- PaperComputers and Education: Artificial Intelligence9 Jun 2026
Human-AI collaboration in higher education: Exploring the impact of technology expectations and distrust
Liana Razmerita, Xiaojiang Zheng, Jonathan P. Allen
Using expectation confirmation theory, this study examines how distrust toward generative AI moderates the relationships between technology expectations, confirmation, and collaboration intentions among 245 higher education students. Findings show that effort and performance expectations positively influence collaboration intentions, but distrust weakens these effects.
Original abstract
: Drawing on expectation confirmation (ECT) theory, this study explores the factors that impact Human-Artificial Intelligence (AI) collaboration and investigates the effect of students' distrust towards GenAI. An online survey was administered to students in higher education using GenAI (N=245). This study found positive and significant relationships between effort expectation and performance technology expectation, as predicted by ECT, with positive confirmation of GenAI collaboration significantly influencing intentions and positively impacting student behavior. This study further revealed that GenAI distrust negatively moderates the relationships between (1) effort expectation and performance expectation; and (2) expectation confirmation toward collaborating with GenAI and intentions to collaborate with GenAI. Accordingly, this research contributes to the understanding of the role of distrust into Human-AI collaboration and extends the theoretical boundaries of ECT in higher education context. Practically, our findings provide insights for educators and GenAI practitioners to develop strategic approaches that effectively bridge students' distrust and initial expectations with responsible Human-AI collaboration behaviors.