Sep 2026· Frontiers in Psychology· Vol 17· 41 references
AI in Service Interactions
Abstract
Introduction Research on generative artificial intelligence (GenAI) in higher education commonly describes use in terms of adoption or frequency. Students, however, may engage with the same systems in different ways. Methods This study conceptualises three GenAI usage orientations (Tool, Guidance, and Collaboration) and surveys 430 college students in China to examine their bivariate and adjusted associations with critical thinking disposition (CTD). Tool represented comparatively delegative use, including task completion centred on GenAI and direct adoption of its output with limited independent revision. Guidance reflected instrumental requests for explanations, examples, clues, or diagnostic assistance. Collaboration reflected iterative coordination directed by students. Based on the measurement evaluation, CTD was used as the primary outcome. Results Tool showed no statistically significant bivariate association with CTD ( r = −0.044, p = 0.363) but had a negative adjusted association ( β = −0.189, p < 0.001). This pattern is consistent with statistical suppression; the adjusted Tool coefficient represents a conditional association. Guidance ( β = 0.126, p = 0.017) and Collaboration ( β = 0.321, p < 0.001) showed positive adjusted associations. The orientation block explained an additional 10.37% of the variance in CTD, and the final model explained 38.95% of CTD variance. Discussion The findings show that behavioural orientations provide information beyond general measures of GenAI experience.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
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Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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