Justice and Fairness Perceptions in Automated Decision-Making—Current Findings and Design Implications

Hellwig, Paul; Maier, Günter W.

doi:10.1007/978-3-031-26104-6_4

Cited by 1 publication

(2 citation statements)

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“…The autonomous attribute of an AI-enabled tool can help identify diverse perspectives in evaluating and performing a task and improve the divergence and creativity of the output in a team learning context (Markauskaite et al, 2022). The perception of independency of AI teammate in HAT activities supports the democratic and unbiased participation of ideas, encouraging knowledge sharing (Hellwig & Maier, 2023). In fact, AI tools offer objective analysis, free from the inherent biases and preconceptions that humans might have, fostering knowledge sharing and team-based learning, which in turn can facilitate creative thinking, shared memory among various team members and peer learning (Jarrahi et al, 2023).…”

Section: Ai Autonomy and Learningmentioning

confidence: 99%

“…Similarly, integrating AI into virtual HATs presents an additional layer of value due to its capacity for independent operation and autonomous contribution (Hellwig & Maier, 2023). Particularly the property of AI that enables it to provide alternative perspectives and real-time feedback (AI autonomy) can have a greater influence on individuals' learning outcomes in higher virtualized environments, where immediate feedback, response, and peer learning are absent (Hilliard et al, 2020).…”

Section: Team Perceived Virtuality and Hat Learningmentioning

confidence: 99%

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Navigating virtual teams in generative AI-led learning: The moderation of team perceived virtuality

Darban

2024

Educ Inf Technol

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This study advances the understanding of Artificial Intelligence (AI)’s role, particularly that of conversational agents like ChatGPT, in augmenting team-based knowledge acquisition in virtual learning settings. Drawing on human-AI teams and anthropomorphism theories and addressing the gap in the literature on human-AI collaboration within virtual teams, this study examines a multi-level, longitudinal model using a sample of 344 graduate students from 48 student project teams in online project-based learning environments. Our model investigates the direct and interactional effects of AI characteristics —autonomy and explainability— and team perceived virtuality (TPV) on the learners’ knowledge-updating process. Findings indicate that embedding AI in learning teams supports knowledge acquisition and learning intentions. The results reveal that while AI explainability significantly enhances knowledge update perceptions, AI autonomy alone does not directly influence knowledge acquisition. Instead, the positive effect of AI autonomy on knowledge updating is contingent upon a high TPV within the team. These findings offer new theoretical insights into AI’s empowering role in educational contexts and provide practical guidance for integrating AI into virtual team learning. This research underlines the importance of designing AI tools with a focus on explainability and leveraging the synergy between AI autonomy and TPV to maximize learning outcomes.

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Section: Ai Autonomy and Learningmentioning

confidence: 99%

Section: Team Perceived Virtuality and Hat Learningmentioning

confidence: 99%