Tag: Artificial Intelligence

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Educational Administration and Leadership Higher Education

Metacognitive support for self-regulated learning in GenAI environment

A recent study by Xu and colleagues investigated the impact of metacognitive support on self-regulated learning (SRL) and learning experiences within generative AI (GenAI) environments. The study addressed concerns about students’ reliance on ChatGPT and the challenges they face in sustaining SRL without guidance.

Participants included 68 sophomore students in a university in China, aged 19–21, majoring in Educational Technology, who had no prior experience with GenAI tools in classroom tasks. The students were randomly divided into two groups: the experimental group received a metacognitive support framework, while the control group did not. Delivered on paper to reduce distractions, the framework encouraged planning, monitoring, and reflecting during tasks, with prompts like setting clear goals, evaluating ChatGPT’s feedback, and reflecting on task performance. Over a four-week period, the participants completed interdisciplinary tasks that integrated knowledge from multiple subjects.

Based on self-report questionnaires, the experimental group demonstrated significant improvements in two of six SRL abilities: in task strategies (ES = +0.69) and self-evaluation (ES = +0.53), while the control group exhibited declines in these areas. Students with metacognitive support also reported lower cognitive load and perceived AI tools as more useful, although these findings were not statistically significant. Academic performance gains were higher in the experimental group (ES = +0.36) but the difference was not statistically significant.

This study suggests that metacognitive support helps students engage with AI tools and develop active learning. Without such guidance, students experience a decline in self-regulation skills which could undermine learning autonomy.

 

Source: Xu, X., Qiao, L., Cheng, N., Liu, H., & Zhao, W. (2025). Enhancing self-regulated learning and learning experience in generative AI environments: The critical role of metacognitive support. British Journal of Educational Technology, 0(0). https://doi.org/10.1111/bjet.13599… Read the rest

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Educational Administration and Leadership Secondary School Education

Can AI reduce teacher workload? Early evidence from a UK trial with ChatGPT

Generative Artificial Intelligence (GenAI) tools like ChatGPT are becoming increasingly common in classrooms—not just for students, but also for teachers. In England, the Department for Education has acknowledged that educators are using GenAI more often to plan lessons, create teaching materials, and even write exam questions. A major reported advantage is the potential to save time, which is especially relevant as workload remains a key factor behind teacher attrition.

To explore whether AI can help reduce this burden, the National Foundation for Educational Research recently conducted a rigorous trial. The study involved 68 secondary schools and 259 science teachers, who were randomly assigned to prepare Year 7 and 8 science lessons either with or without ChatGPT. Teachers in the ChatGPT group were given a practical guide to support their use of the tool. Over a 10-week period in the summer term of 2024, they logged how much time they spent preparing lessons, with a particular focus on weeks 6 to 10—after an initial adaptation phase.

The findings were encouraging. On average, teachers using ChatGPT spent 25 minutes less per week on lesson preparation than those in the non-AI group—56 minutes versus 81.5—representing a 31% time saving. Importantly, an independent expert panel found no difference in the quality of lesson materials between the two groups.

Use of the support guide also declined over time, suggesting that teachers grew more confident in integrating the tool into their practice. Looking ahead, future research could explore how GenAI tools like ChatGPT are used for other aspects of teachers’ work—such as administrative duties—and whether their impact differs across subjects or age groups, especially as new and more advanced versions continue to roll out.

 

Source (Open Access): Roy, P., Poet, H., Staunton, R., Aston, K., & Thomas, D. (2024). ChatGPT in lesson preparation—A Teacher Choices Trial. National Foundation for Educational Research. https://www.isrctn.com/ISRCTN13420346… Read the rest

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Higher Education Language Development

Will GenAI Chatbot Enhance Self-Regulated Learning and Reading Engagement for EFL Students?

A quasi-experiment by Pan and colleagues explored whether a generated AI chatbot, Reade, could enhance self-regulated learning (SRL) in reading strategy and reading engagement among English as a foreign language (EFL) students. The study involved 61 first-year English major students (over 80% female, mean age = 18.5) at a university in eastern China. Two classes were randomly assigned to a treatment (n=31) or a control group (n=30).

Using the research team-developed ReadMate platform, powered by ChatGPT 3.5 Turbo, students accessed Reade, which had two main functions: (1) recommending reading materials based on student proficiency and preferences, and (2) supporting self-regulated reading strategies through a Plan-Enact-Reflect approach. At the “Enact” phase, the treatment group students had access to full functions in ReadMate, including GenAI-empowered SRL interaction for further guidance on SRL strategies, while the control group used ReadMate without this feature. The 12-week study required participants to complete reading tasks via ReadMate as part of their coursework. Questionnaires reported self-regulated reading strategy use and reading engagement before and three weeks after the intervention.

After controlling for baseline scores, the results revealed that the treatment group significantly outperformed the control group in SRL strategy use across metacognitive, cognitive, motivational, and behavioral dimensions. Platform logs indicated that the treatment group accessed Planning Strategies and Self-Checklist panels more frequently. Additionally, the treatment group exhibited higher reading engagement across cognitive, behavioral, and emotional dimensions, as well as greater time and effort invested in reading tasks on the platform.

Although the study highlights the potential of GenAI chatbots like Reade in supporting SRL and reading engagement, its reliance on a small sample and self-reported data underscores the need for more rigorous research to explore AI’s role in enhancing EFL students’ reading outcomes.

 

Source: Pan, M., Lai, C., & Guo, K. (2025). Effects of GenAI-empowered interactive support on university EFL students’ self-regulated strategy use and engagement in reading. The Internet and Higher Education, 65, 100991. https://doi.org/10.1016/j.iheduc.2024.100991… Read the rest

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Educational Administration and Leadership Higher Education

A comparative study of learning effects from human-made and AI-generated teaching videos

In the age of generative artificial intelligence (AI), can generative AI-made teaching videos deliver learning outcomes comparable to those of human-made videos? A recent study explored this question by comparing the effectiveness of four generative AI-made videos with four human-made videos on the same topics in a management course. The online intervention involved 447 US-based laypeople, most of whom had completed higher education. The study included two experimental conditions: in the first, 213 participants watched four teaching videos in a human-AI-human-AI sequence; in the second, 234 participants watched them in an AI-human-AI-human sequence. After each video, participants completed a survey rating their learning experience, followed by a multiple-choice exam to assess learning outcomes.

Results showed that human-made videos provided a small but statistically significant advantage in learning experience, suggesting that the participants still preferred human teachers in the videos. Regarding learning outcomes, participants in both conditions achieved a similar degree of acquired knowledge. The authors noted that as generative AI-made teaching videos become increasingly common due to their ease of production, researchers and educators should critically explore ways to use generative AI-made content to improve learning experiences, while being mindful of potential limitations and ethical considerations in production and use.

 

Source (Open Access): Netland, T., von Dzengelevski, O., Tesch, K., & Kwasnitschka, D. (2025). Comparing human-made and AI-generated teaching videos: An experimental study on learning effects. Computers & Education, 224, 105164. https://doi.org/10.1016/j.compedu.2024.105164… Read the rest

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Educational Administration and Leadership Higher Education Social and Motivational Outcomes

Low academic self-efficacy may lead to AI dependency through stress

Generative AI tools are emerging in classrooms and on student laptops across the globe. Policymakers, educators, and other influencers must understand the negative consequences of becoming unhealthily dependent on AI. Using the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, which examines how psychological factors contribute to problematic technology use, researchers studied AI dependency among 300 university students in Seoul. While they hypothesized a link between self-efficacy (students’ belief in their abilities) and AI dependency, they found no direct association. Instead, academic stress emerged as the key driver in AI dependency among students. In other words, the more academically stressed a student is, the more likely they are to become dependent on AI tools.

Notably, 84% of the students surveyed used ChatGPT for academic help, though not all showed signs of dependency. The researchers also sought to discover the negative consequences of AI dependency. Students reported that AI dependency led to increased laziness, the spread of misinformation, a lower level of creativity, and reduced critical and independent thinking. The researchers also examined the role of performance expectations: students who perceived that AI would help their performance were more likely to become dependent.

The emergency of AI tools in schools is a conundrum that generates many different opinions and policy recommendations. Rather than focusing primarily on AI restrictions and teaching AI literacy, schools may need to prioritize stress management and low-stakes practice opportunities to reduce AI dependency.

 

Source (Open Access): Zhang, S., Zhao, X., Zhou, T., & Kim, J. H. (2024). Do you have AI dependency? The roles of academic self-efficacy, academic stress, and performance expectations on problematic AI usage behavior. International Journal of Educational Technology in Higher Education, 21(1), 34. https://doi.org/10.1186/s41239-024-00467-0… Read the rest

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Effective Teaching Approach K-12 Education Maths and Science Learning

Empowering tutoring expertise with AI

Effective tutoring can significantly improve student learning outcomes, but many students, particularly in under-served communities, often lack access to high-quality, expert-guided instruction due to resource limitations and the scarcity of trained educators. Stanford University researchers conducted the first randomized controlled trial of Tutor CoPilot, a Human-AI system designed to provide real-time, expert-like guidance to K-12 tutors, to explore its impact on enhancing tutor effectiveness during live sessions. In collaboration with FEV Tutor and a U.S. Southern school district, the researchers conducted an intervention involving 900 tutors and 1,800 students from Title I schools participating in an in-school, virtual tutoring program focused on mathematics.

The study showed that students whose tutors used Tutor CoPilot were 4 percentage points more likely to master mathematical lesson topics compared to those in the control group. This effect was especially pronounced among students taught by lower-rated tutors, whose mastery improved by 9 percentage points. The system also promoted the use of expert teaching strategies, such as prompting students to explain their reasoning and asking guiding questions, rather than giving away answers, fostering deeper student understanding. Despite some challenges, such as occasional misalignment of AI suggestions with student grade levels, tutors reported that Tutor CoPilot helped them better address student needs. With an annual cost of just $20 per tutor, Tutor CoPilot offers a scalable and affordable path to improving tutoring quality in contexts where expert educators are in short supply. This study illustrates the potential of Human-AI systems like Tutor CoPilot to make high-quality learning accessible to all students.

 

Source (Open Access): Wang, Rose E., Ribeiro, Ana T., Robinson, Carly D., Loeb, Susanna, & Demszky, Dorottya. (2024). Tutor copilot: A human-AI approach for scaling real-time expertise. (EdWorkingPaper: 24 -1056). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/81NH-8262… Read the rest