Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices
Abstract
:1. Introduction
2. Autonomy in Language Learning
2.1. Foundations of Learner Autonomy
2.2. Autonomy in Digital Environments
2.3. Autonomy in the Age of AI
3. Methodology
3.1. Research Questions
- How do language learners with different backgrounds experience and develop autonomy within a course that supports self-directed learning and incorporates ChatGPT?
- How do learners develop and adapt strategies for self-directed learning in an AI-mediated environment?
- How do learners develop agency while integrating AI tools into their learning process?
3.2. Course Structure
3.2.1. Phase 1: Self-Discovery and Goal Orientation (Weeks 1–6)
- Language Learning Biography: Students reflected on past language learning experiences, motivations, and influences to establish a foundation for strategic development.
- Self-Assessment of Language Skills: Using the CEFR scale, students assessed their proficiency across different language skills, identifying strengths and areas for improvement.
- Strategy Inventory for Language Learning (SILL): Through an adapted SILL, students analyzed their learning strategies, identified gaps, and considered the potential of AI to support their development.
- Experiences with AI Tools: Students documented and evaluated their initial experiences with AI, considering its effectiveness, limitations, and suitability for language learning.
- Planning for the Language Challenge: Drawing on their previous reflections, students created personalized learning plans, outlining language goals, strategies, and AI use. These plans were refined through peer discussion.
3.2.2. Phase 2: Self-Study Period and Language Challenge (Weeks 7–11)
3.2.3. Phase 3: Weeks 12–13—Presentations and Final Reflections
3.3. Participants
3.4. Data Collection
- Reflective Journals: Six journal entries provided a detailed account of each learner’s reflections, strategies, and responses to AI tool use throughout the course.
- Wakelet Portfolios: The digital portfolios provided both narrative and visual records of students’ engagement with AI tools and their strategy adaptations throughout the Language Challenge.
- Semi-Structured Interviews: Two interviews were conducted at the end of the semester: a group interview with five students, including three of the participants, and an individual interview with one participant. Each interview lasted approximately 60 min, focusing on reflections about autonomy, AI engagement, and strategic use of ChatGPT in learning processes. Interviews were recorded and transcribed using Otter.ai.
3.5. Data Analysis
- For the individual case analysis, each participant’s data set (journal entries, Wakelet portfolios, and interview transcripts) was analyzed chronologically to trace their development of autonomy and engagement with AI tools throughout the course. This process enabled the construction of detailed narrative profiles that captured each learner’s unique journey, challenges, and growth.
- For the cross-case analysis, the individual profiles were then compared to identify patterns in how learners engaged with AI tools and developed autonomy. This analysis revealed key dimensions of AI-mediated autonomy, including foundational understanding, practical engagement, and critical evaluation.
3.6. Ethical Considerations
4. Findings
4.1. Profiles
4.1.1. Sienna’s Profile
Language Learning Background and Self-Assessment
The Challenge: Improving Accuracy with Games
Reflections on Development
4.1.2. Gina’s Profile
Language Learning Background and Self-Assessment
The Challenge: Extensive Reading with Harry Potter
Evolution with AI Tools
Reflections on Development
4.1.3. Cora’s Profile
Language Learning Background
The Challenge: From Cold War to Conversation
Reflections on Development
4.1.4. Flora’s Profile
Language Learning Background
Learning Challenge: Focus on Listening
Development and Final Reflections
4.2. Cross-Case Analyses
4.2.1. Development of Self-Directed Learning Strategies
Initial Approaches to AI Integration
Development of Personalized Learning Processes
AI Integration
4.2.2. Developing Agency and Critical Engagement
Critical Evaluation of AI Output
Evolving Learner–AI Relationships
Maintaining and Developing L2 Voice
5. Discussion
5.1. Reconceptualising Autonomy
5.2. Implications and Future Directions
- How learners engage with AI systems that increasingly adapt to user behavior.
- The long-term impact of AI integration on learner agency and language development.
- How learner attitudes and practices with AI evolve over time.
- Ways to support learner autonomy as AI functionalities expand.
- The role of peer collaboration in developing critical approaches to AI use.
5.3. Limitations and Concluding Reflections
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Alm, A. Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices. Educ. Sci. 2024, 14, 1369. https://doi.org/10.3390/educsci14121369
Alm A. Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices. Education Sciences. 2024; 14(12):1369. https://doi.org/10.3390/educsci14121369
Chicago/Turabian StyleAlm, Antonie. 2024. "Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices" Education Sciences 14, no. 12: 1369. https://doi.org/10.3390/educsci14121369
APA StyleAlm, A. (2024). Exploring Autonomy in the AI Wilderness: Learner Challenges and Choices. Education Sciences, 14(12), 1369. https://doi.org/10.3390/educsci14121369