AI as a Tool for Success: Preservice Teachers’ Journey Continues

Framework

Our research follows three theoretical frameworks: Constructivism, Innovation Adoption Theories, and Universal Design for Learning. Constructivism, which emphasizes the active role of learners in constructing their own understanding and knowledge through experiences and interactions. This perspective aligns with the following key principles:
1. Active Learning: Encouraging preservice teachers to engage with AI tools actively, experimenting and reflecting on their use in real-world teaching scenarios.
2. Social Constructivism: Promoting collaboration among preservice teachers, mentors, and students to co-construct knowledge and share diverse perspectives on AI integration.
3. Experiential Learning: Focusing on hands-on, practical experiences where preservice teachers apply AI tools in their teaching practice, learning through doing and reflecting on their experiences.
4. Reflective Practice: Encouraging continuous reflection on the use of AI in teaching, helping educators to critically evaluate and improve their practices.
Innovation Adoption Theories
1. Technology Acceptance Model (TAM): This model helps us understand how preservice teachers perceive and accept AI tools. By examining factors such as perceived ease of use and perceived usefulness, we can identify barriers and facilitators to AI adoption in educational settings. Our research aims to enhance these perceptions, making AI tools more accessible and valuable for educators.
2. Diffusion of Innovations Theory: This theory provides a framework for understanding how AI tools spread among preservice teachers and within educational institutions. By identifying the stages of adoption (knowledge, persuasion, decision, implementation, and confirmation), we can develop strategies to support each stage, ensuring a smoother integration of AI into teaching practices.
Universal Design for Learning (UDL)
Our research also incorporates the principles of Universal Design for Learning (UDL) to ensure that AI tools are accessible and beneficial to all learners. UDL emphasizes:
1. Multiple Means of Engagement: Using AI to provide various ways to engage students, catering to different interests, motivations, and learning preferences.
2. Multiple Means of Representation: Leveraging AI to present information in diverse formats (e.g., text, audio, video) to accommodate different learning styles and needs.
3. Multiple Means of Action and Expression: Allowing students to demonstrate their knowledge and skills through various methods, supported by AI tools that offer alternative ways to complete tasks and assessments.
By integrating these frameworks, our research aims to provide a comprehensive approach to understanding and enhancing the use of AI in education, ensuring it is effective, inclusive, and widely adopted.

Methods

Data Sources:
1. Participants: Student teachers, first year teachers, and their students in various K-12 classrooms (9 student teachers, 12 first year teachers, and 150 students).
2. Surveys: Pre- and post-implementation surveys for student teachers to gauge their knowledge, attitudes, and self-efficacy regarding AI
3. Surveys: Completion survey for first year teachers to gauge their use of, support of, and integration of AI tools in their teaching.
4. Interviews: Semi-structured interviews with student teachers to understand their experiences and challenges using AI.
5. Student Work: Collection of student assignments, projects, and assessments completed with AIs assistance.
Methods of Analysis:
1. Participant Selection: Student teachers will be selected from a diverse range of institutions, grade levels, and subject areas, ensuring representation.
2. Pre-Implementation Assessment: Student teachers will take a pre-implementation survey to assess their baseline knowledge and attitudes toward AI.
3. Training: Student teachers will undergo a training program covering AI’s capabilities, ethical considerations, and pedagogical integration.
4. Classroom Implementation: Student teachers will integrate AI into their lessons throughout the academic year, with observations and student work collection.
5. Post-Implementation Assessment: After the implementation period, student teachers will complete a post-implementation survey to assess changes in knowledge and attitudes.
6. Interviews: Qualitative data from interviews will be transcribed and analyzed thematically to identify patterns and challenges faced by student teachers.
7. Student Work Analysis: Student assignments and projects will be analyzed for quality, originality, and the extent of AI's impact on outcomes.
8. Data Integration: The quantitative data from surveys and qualitative data from interviews will be integrated to provide a comprehensive understanding of the study.
9. Ethical Considerations: Ethical considerations and challenges related to AI usage will be examined in-depth.
Research Questions:
1. What is the impact of AI integration on student learning outcomes?
• Analyze student work to assess changes in quality and depth of learning.
2. How does AI affect student engagement and motivation in the classroom?
• Examine classroom observations and survey responses.
3. What are the challenges and opportunities faced by student teachers when integrating AI into their instruction?
• Extract insights from interviews with student teachers.
4. To what extent do student teachers' knowledge, attitudes, and self-efficacy regarding AI change after training and implementation?
• Compare pre- and post-implementation survey data.

Results

Expected Results:
1. Student Learning Outcomes: We anticipate that the integration of AI into classrooms will positively impact student learning outcomes. We expect to see improvements in the quality and depth of student work, as AI provides personalized support and resources to enhance their understanding of course material.
2. Student Engagement and Motivation: It is expected that AI's interactive and responsive nature will increase student engagement and motivation in the classroom. Preliminary observations and survey responses suggest that students find the tool engaging and valuable for their learning.
3. Challenges and Opportunities: Qualitative analysis of interviews with student teachers is likely to reveal both challenges and opportunities. Common challenges may include initial resistance to AI technology, concerns about privacy, and the need for ongoing technical support. Opportunities may include innovative lesson planning and adapting to diverse learning needs.
4. Change in Student Teachers' Knowledge and Attitudes: We anticipate that student teachers' knowledge, attitudes, and self-efficacy regarding AI will significantly improve after training and implementation. Pre-implementation surveys indicate a baseline understanding, which we expect to evolve positively based on post-implementation survey data.
Status of Research:
As of the proposal submission, the research study is actively underway. The pre-implementation phase, including participant selection, training, and initial classroom integration was completed in January 2024. This will be replicated in January 2025 with the second group of student teachers at the EPP. Data collection, including observations, surveys, and interviews, will be ongoing throughout the Spring 2025 semester. The completer survey will be sent out to the 2024 program completers in March 2025 with an anticipated response time of April 2025.
While we have the statistical analysis of quantitative data and in-depth qualitative analysis of interview from 2024, the 2025 information will be incorporated upon semester completion, May 2025.
The research is expected to be fully implemented and completed according to the outlined timeline.

Importance

Educational Importance:
1. Enhancing Student Learning: The study explores the impact of AI integration on student learning outcomes. Understanding how AI technologies can positively influence student performance is crucial in the modern educational landscape.
2. Fostering Innovation: By showcasing innovative pedagogical strategies, the study encourages educators to think creatively about technology integration. It promotes a growth mindset, pushing the boundaries of traditional teaching methods.
3. Ethical Considerations: With an in-depth examination of ethical AI usage, the study contributes to ethical discussions in education. It raises awareness about responsible technology integration and data privacy, ensuring educators make informed choices.
4. Inclusivity: The study addresses the diverse needs of learners, emphasizing Universal Design for Learning principles. It provides insights into how AI can be customized to accommodate different learning styles and abilities, fostering inclusivity.
5. Teacher Preparation: Preparing student teachers to effectively use AI tools aligns with the broader goal of equipping educators with 21st-century skills. It ensures that future educators are tech-savvy and ready to meet the demands of modern classrooms.
Scientific Importance:
1. Empirical Evidence: The study contributes empirical evidence to the relatively new field of AI in education. It helps build a foundation of research-based knowledge about the benefits and challenges of AI integration in K-12 settings.
2. Validated Best Practices: By analyzing data from diverse classrooms, the study has the potential to identify best practices for integrating AI into different subject areas and grade levels, offering valuable guidance to educators.
3. Generalizability: The study's mixed-methods approach and diverse participant pool enhance the generalizability of the findings. The insights gained can inform educational practices beyond the study's specific context.
Value to ISTE Attendees:
1. Practical Insights: Attendees at ISTE 2025 will gain practical, evidence-based insights into using AI technology like AI effectively in their classrooms, aligning with ISTE's focus on technology integration.
2. Professional Development: Educators, including student teachers, will benefit from professional development opportunities presented in the study, helping them stay current in their field.
3. Ethical Considerations: Attendees will gain a deeper understanding of the ethical implications of AI in education, empowering them to make responsible technology choices.
4. Innovation Inspiration: The study encourages innovative thinking and provides concrete examples of how to harness AI for educational purposes, inspiring attendees to explore creative teaching approaches.
In summary, the study's educational and scientific significance lies in its potential to enhance student learning, foster innovation, address ethical considerations, promote inclusivity, and prepare teachers for the digital age. Its value to ISTE attendees is in providing practical, research-based guidance and inspiring educators to leverage AI for the benefit of their students and classrooms.

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