arXiv subjects

Janne Rotter

Publications and source records attributed to Janne Rotter.

3 recordsLinked to original sources

arXiv2026

Experiential Versus Instructional Approaches for Eliciting Metacognitive Awareness in AI-Assisted Learning: A Short-Term Longitudinal Study

With generative AI (GenAI) entering classrooms the question to which teaching approach best supports metacognitive skill acquisition in AI-assisted learning becomes pressing. In this short-term longitudinal study we investigate two contrasting approaches: experiential learning encompassing hands-on approaches and instructional learning such as classical lectures. We conducted a quasi-experiment with 126 university students from a first-year engineering course which were distributed across the two conditions and completed a two hour session on learning with GenAI in the corresponding learning style. Metacognitive awareness which encompasses both knowledge of cognition (understanding effective AI-use strategies) and regulation of cognition (applying that knowledge in practice) was measured before and after the session. Additionally, students longitudinal metacognitive awareness was tracked over the trimester and assessed again five weeks after the initial intervention. Results reveal that experiential methods outperform instructional approaches in engagement and knowledge of cognition immediately after the intervention. By five weeks, the two groups converged on these measures, while the experiential group showed a delayed, continuous within-group increase in regulation of cognition that was not observed in the instructional group. This suggests that the benefits of experiential approaches extend beyond conventional educational settings to AI-assisted learning, and that knowledge and regulation may develop on different timescales under experiential learning.

cs.HC↗

arXiv2026

Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education

In recent years, generative AI (GenAI) in educational settings has become ubiquitous in university students' daily lives, despite its potential to induce over-reliance, metacognitive disengagement, and diminished learning when used unrestrictedly. While most prior research has focused on how to pedagogically scaffold its usage, the question of when to allow off-the-shelf GenAI remains understudied and lacks pedagogically grounded empirical investigation. We treat access timing itself as a form of implicit scaffolding and operationalize it through a reinforcement learning (RL) agent that decides when students should access GenAI, with a reward function grounded in metacognitive theory, cognitive load theory, and productive failure. In a mixed-methods controlled lab study with N=105 higher education students, we compared the agent's effect on learning gains and metacognitive engagement to unrestricted and fully restricted use. Results show that strategically timed GenAI access under the reinforcement learning condition improved objective post-test performance and metacognitive accuracy compared with unrestricted access, without requiring explicit metacognitive prompts or structured scaffolding. Exploratory comparisons with the fully restricted condition further suggest that timed access may reduce task errors and time on task relative to complete withholding. Overall, timing of GenAI access therefore is a tractable, theoretically grounded, and scalable pedagogical strategy that improves over completely unrestricted and withheld access, compatible with off-the-shelf tools and potentially low adoption barrier. This opens up a new research area that explores how access timing can be facilitated by educators and implemented in human-AI learning system design.

cs.CY↗

arXiv2025

AI Adoption in NGOs: A Systematic Literature Review

AI has the potential to significantly improve how NGOs utilize their limited resources for societal benefits, but evidence about how NGOs adopt AI remains scattered. In this study, we systematically investigate the types of AI adoption use cases in NGOs and identify common challenges and solutions, contextualized by organizational size and geographic context. We review the existing primary literature, including studies that investigate AI adoption in NGOs related to social impact between 2020 and 2025 in English. Following the PRISMA protocol, two independent reviewers conduct study selection, with regular cross-checking to ensure methodological rigour, resulting in a final literature body of 65 studies. Leveraging a thematic and narrative approach, we identify six AI use case categories in NGOs - Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization - and extract common challenges and solutions within the Technology-Organization-Environment (TOE) framework. By integrating our findings, this review provides a novel understanding of AI adoption in NGOs, linking specific use cases and challenges to organizational and environmental factors. Our results demonstrate that while AI is promising, adoption among NGOs remains uneven and biased towards larger organizations. Nevertheless, following a roadmap grounded in literature can help NGOs overcome initial barriers to AI adoption, ultimately improving effectiveness, engagement, and social impact.

cs.CY↗