Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?

• URL: http://arxiv.org/abs/2508.10919v1
• Date: Sun, 03 Aug 2025 11:43:01 GMT
• Title: Human-AI collaboration or obedient and often clueless AI in instruct, serve, repeat dynamics?
• Authors: Mohammed Saqr, Kamila Misiejuk, Sonsoles López-Pernas,
• Abstract summary: This study examines human-AI interactions while solving a complex problem.<n>Findings reveal a dominant Instructive pattern with interactions characterized by iterative ordering rather than collaborative negotiation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While research on human-AI collaboration exists, it mainly examined language learning and used traditional counting methods with little attention to evolution and dynamics of collaboration on cognitively demanding tasks. This study examines human-AI interactions while solving a complex problem. Student-AI interactions were qualitatively coded and analyzed with transition network analysis, sequence analysis and partial correlation networks as well as comparison of frequencies using chi-square and Person-residual shaded Mosaic plots to map interaction patterns, their evolution, and their relationship to problem complexity and student performance. Findings reveal a dominant Instructive pattern with interactions characterized by iterative ordering rather than collaborative negotiation. Oftentimes, students engaged in long threads that showed misalignment between their prompts and AI output that exemplified a lack of synergy that challenges the prevailing assumptions about LLMs as collaborative partners. We also found no significant correlations between assignment complexity, prompt length, and student grades suggesting a lack of cognitive depth, or effect of problem difficulty. Our study indicates that the current LLMs, optimized for instruction-following rather than cognitive partnership, compound their capability to act as cognitively stimulating or aligned collaborators. Implications for designing AI systems that prioritize cognitive alignment and collaboration are discussed.

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