ArticleScientific reports2025
Evaluating AI-Powered Applications for Enhancing Undergraduate Students' Metacognitive Strategies, Self-Determined Motivation, and Social Learning in English Language Education.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Developing metacognitive knowledge for effective AI-supported study behaviors in undergraduate biology: recommendations for instructors.Journal of microbiology & biology education · 2026Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI) technologies are transforming educational settings by offering tools that enhance learning experiences. AI-powered applications, such as ChatGPT and Poe, provide real-time assistance, fostering learner autonomy and self-determined motivation. However, limited research has explored their impact on undergraduate students' learning strategies and motivation. This study investigates the effectiveness of AI-powered educational applications in enhancing metacognitive and social learning strategies, as well as self-determined motivation, among Chinese undergraduate students. This mixed-methods quasi-experimental study involved 310 undergraduates (45% female, 55% male; M age = 21) at the Criminal Investigation Police University of China. Participants were assigned to an AI-integrated experimental group (n = 139) or a control group (n = 171). Validated questionnaires assessed metacognitive/social strategies (SILL) and autonomous motivation (RAI). Qualitative data from 834 reflective journals were thematically analyzed. ANCOVA was used to compare post-test outcomes, controlling for pre-test scores, while journals provided experiential insights. Ethical approval and informed consent were obtained. Using a quasi-experimental design, this study investigated the impact of AI-integrated instruction on metacognitive strategies, social strategies, and motivation in 310 undergraduate students. ANCOVA revealed significant improvements in the AI group (p < .001), with large effect sizes observed for metacognition (η² = 0.39) and motivation (η² = 0.31), which are large effect sizes according to Cohen's benchmarks. Qualitative analysis of 834 journals highlighted themes of autonomy, support for metacognitive strategies, and reduced anxiety, although risks of superficial application were noted. Mixed methods confirmed AI's effectiveness in enhancing strategic learning. AI applications facilitate independent academic exploration and enhance learners' motivation by providing immediate support and personalized learning experiences. These findings highlight the potential of AI-powered tools to foster learner autonomy. However, successful integration into educational settings requires strategic pedagogical approaches to maximize benefits while addressing potential challenges.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.