Scientific Reports (2025) 15:35199
Evaluating AI-Powered Applications for Enhancing Undergraduate Students' Metacognitive Strategies, Self-Determined Motivation, and Social Learning in English Language Education
Authors: Yue Zhai & Behzad Nezakatgoo
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.
Executive Impact & Key Findings
This study evaluated the effectiveness of AI-powered applications in enhancing undergraduate students' metacognitive strategies, self-determined motivation, and social learning in English language education. A mixed-methods quasi-experimental design with 310 Chinese undergraduates showed significant improvements in the AI-integrated experimental group. Quantitative ANCOVA revealed large effect sizes for metacognition (η² = 0.39) and motivation (η² = 0.31). Qualitative analysis of 834 reflective journals reinforced these findings, highlighting themes of autonomy, metacognitive support, and reduced anxiety, while also noting risks of superficial application. The findings emphasize AI's potential to foster learner autonomy and motivation but underscore the need for strategic pedagogical approaches to maximize benefits and mitigate challenges.
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Students Participants
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Metacognition Effect Size (η²)
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Motivation Effect Size (η²)
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Reflective Journals Analyzed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Metacognitive Strategies
AI tools significantly boosted metacognitive strategy use among students, fostering greater awareness of progress, increased goal-setting, and improved reflective practices.
0.39
Effect Size (η²) for Self-Directed Reading
| Strategy Item | AI Group (Adjusted Mean) | Control Group (Adjusted Mean) | Significance (P) | Effect Size (η²) |
|---|---|---|---|---|
| I will learn how to improve my English skills. | 4.08 | 3.21 | <0.001 | 0.35 |
| I pay attention when someone is speaking English. | 4.18 | 3.45 | <0.001 | 0.27 |
| I have noticed my English mistakes and use that information... | 4.21 | 3.34 | <0.001 | 0.32 |
| I think about my progress in learning English. | 4.09 | 3.22 | <0.001 | 0.29 |
| I find many ways to use my English. | 4.14 | 3.33 | <0.001 | 0.34 |
| I have clear goals for improving my English skills. | 4.17 | 3.50 | 0.002 | 0.21 |
| I look for people I can talk to in English. | 4.22 | 3.48 | <0.001 | 0.36 |
| I look for opportunities to read as much as possible in English. | 4.28 | 3.42 | <0.001 | 0.39 |
Social Learning Strategies
AI-supported instruction led to significant improvements in students' social learning strategies, particularly in help-seeking behavior and interactive engagement.
0.29
Effect Size (η²) for Help-Seeking from English Speakers
| Strategy Item | AI Group (Adjusted Mean) | Control Group (Adjusted Mean) | Significance (P) | Effect Size (η²) |
|---|---|---|---|---|
| Ask others to slow down or repeat | 4.18 | 3.52 | 0.028 | 0.18 |
| Ask questions in English | 4.09 | 3.41 | 0.009 | 0.23 |
| Practice English with other students | 4.11 | 3.29 | 0.006 | 0.25 |
| Ask English speakers to correct me | 3.49 | 3.03 | 0.045 | 0.14 |
| Learn about English culture | 3.46 | 2.90 | 0.022 | 0.20 |
| Ask for help from English speakers | 3.85 | 3.07 | 0.004 | 0.29 |
Self-Determined Motivation
The AI intervention significantly enhanced all dimensions of self-determined motivation, especially intrinsic and autonomous motivation, fostering enjoyment, curiosity, and internal regulation.
0.31
Effect Size (η²) for Intrinsic Motivation
| Motivation Type | AI Group (Adjusted Mean) | Control Group (Adjusted Mean) | Significance (P) | Effect Size (η²) |
|---|---|---|---|---|
| Intrinsic Motivation | 5.69 | 4.88 | <0.001 | 0.31 |
| Extrinsic Motivation | 5.58 | 4.79 | 0.002 | 0.25 |
| Autonomous Motivation | 5.63 | 4.84 | 0.001 | 0.27 |
AI Integration Process
Qualitative analysis revealed key themes on how students integrate AI into academic routines, utilize it for metacognitive development, and experience motivational shifts, reinforcing AI's role as a strategic learning amplifier.
Enterprise Process Flow
Structured Workshop Training on AI Tools (ChatGPT, Poe, Google Bard)
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Encouraged Use for Language Learning Activities (Summarizing, Revising, Vocabulary, Goal Setting)
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Students Document Experiences in Reflective Journals
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Instructors Monitor Engagement & Provide Feedback
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AI Becomes Habitual, Used for Planning, Monitoring & Evaluating
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Reduced Anxiety, Increased Confidence & Strategic Revision
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Enhanced Motivation, Enjoyment & Self-Regulation
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Exploration of Cultural Dimensions & Curiosity
Real-World Impact: Enhancing Learner Autonomy with AI
Students in the experimental group seamlessly integrated AI tools into their daily academic routines, transforming their learning experience. For instance, Student 1 reported, "I used ChatGPT every Sunday night to prepare for my weekly assignments." This routine use extended to metacognitive scaffolding, where Student 7 observed, "The tool showed me how to structure my thoughts before writing," and Student 8 added, "I started using Poe to reflect on why I made certain mistakes." This indicates AI's role in cultivating reflective self-regulation. Moreover, AI significantly reduced language anxiety, with Student 10 sharing, "The AI makes me feel like I have a safety net while practicing speaking." These examples highlight AI's potential to foster self-directed learning and motivation, despite noted risks of superficial application.
Outcome: AI applications, when integrated strategically, empower students with greater autonomy and self-regulation, making learning more engaging and less daunting. This leads to a proactive approach in language acquisition and cultural exploration, shifting from passive consumption to active engagement.
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Your AI Implementation Roadmap
Based on the study's insights and best practices, here's a strategic roadmap for integrating AI into your educational framework.
Phase 1: Strategic Curriculum Integration
Embed AI tools into reflective tasks (goal-setting, planning, self-assessment) and the iterative writing process, shifting from isolated use to integral components of metacognitive learning.
Phase 2: Digital Literacy & Ethical Use Instruction
Provide explicit digital literacy training, teaching students to critically evaluate AI-generated content and avoid over-reliance for superficial task completion.
Phase 3: Fostering Social Language Practice
Utilize AI to create safe, low-pressure environments for rehearsing conversations, scripting questions, and practicing clarification requests before live interactions.
Phase 4: Structured Reflection & Autonomy
Incorporate regular journaling and guided reflections into the curriculum to document learning processes and ensure AI integration aligns with pedagogical goals.
Phase 5: Institutional Readiness & Scalability Assessment
Evaluate institutional readiness, faculty training needs, and technological infrastructure for widespread AI adoption, considering local contexts and resource limitations.
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