Education 3.0 – AI and Gamification Tools for Increasing Student Engagement and Knowledge Retention

Vrabie, Catalin

doi:10.1007/978-3-031-43590-4_5

Cited by 1 publication

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“…Lastly, exploring students' learning performance in conjunction with their trust in AI technology may yield insights into the effectiveness of AI-driven educational interventions. Learning performance serves as an essential and tangible measure of the impact of AI technologies on academic achievement (Lee et al, 2022), skill acquisition (Benvenuti et al, 2023), and knowledge retention (Vrabie, 2023). Analyzing how variations in trust affect learning outcomes or vice versa would identify factors that enhance or hinder learners' engagement and success when using AI-powered learning support tools.…”

Section: User Aspectsmentioning

confidence: 99%

How Learners’ Trust Changes in Generative AI Over a Semester of Undergraduate Courses

Song

2024

Preprint

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Artificial intelligence (AI) applications, including advanced machine learning (ML), have received attention in education, and generative AI-powered chatbots like ChatGPT and Copilot have been adopted in diverse educational settings worldwide. However, the actual use of and perception regarding AI-powered chatbots by learners have been under-investigated. Obtaining a more accurate understanding of learners’ perceptions, particularly their trust in AI-powered technology, is crucial for preparing for future education because learners’ trust in the technology itself is significantly related to successful technology adoption in various educational contexts. To address this issue, we focused on undergraduate students’ trust in AI-powered chatbots within their courses and investigated the relationship between their trust levels and learning performance. Additionally, we explored the potential association between learners’ trust levels, self-regulated learning, and computational thinking skills. This research employed an exploratory study design with a regular course setting, and there were no experimental treatments involved. In the results, we found that learners’ trust levels were not correlated with their computational thinking, self-regulated learning, or learning performance. Furthermore, these constructs (i.e., self-regulated learning, computational thinking, and learning performance) did not significantly predict learners’ trust in AI. However, there was a notable difference between high and low performers concerning changes in trust over time. Trust levels among low performers exhibited a significant change over the semester, whereas those of high performers remained relatively stable. The study suggests that expectations regarding trusting generative AI technology can be influenced by trusting intention through performance.

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Section: User Aspectsmentioning

confidence: 99%

How Learners’ Trust Changes in Generative AI Over a Semester of Undergraduate Courses

Song

2024

Preprint

View full text Add to dashboard Cite

show abstract

Education 3.0 – AI and Gamification Tools for Increasing Student Engagement and Knowledge Retention

Cited by 1 publication

References 16 publications

How Learners’ Trust Changes in Generative AI Over a Semester of Undergraduate Courses

How Learners’ Trust Changes in Generative AI Over a Semester of Undergraduate Courses

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