Preparing preservice teachers to implement AI-powered personalized learning in special education through professional development in technical and pedagogical expertise¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The dissertation's third manuscript provides a guide for preservice teacher faculty on integrating AI-Powered Personalized Learning (AI-PPL) in special education. It offers practical strategies for incorporating AI into special education environments, where AI-PPL tools offer tailored learning experiences and adaptive feedback that enhance engagement for students with disabilities, especially in STEM subjects. The chapter emphasizes that "teachers' need to gain technical and pedagogical expertise to implement AI tools effectively," positioning professional development as central to successful integration. It also stresses ethical considerations of ensuring accessibility and inclusivity when integrating AI in educational settings, and argues effective preservice training can reshape how future educators implement personalized learning strategies.

Design Implications¶

Context¶

Requirements¶

• Professional development building teachers' technical and pedagogical expertise with AI tools
• Attention to ethical considerations including accessibility, inclusivity, data privacy, and algorithmic bias

Constraints¶

• The article notes additional research is necessary to substantiate the effects of AI on student learning outcomes

Target Learners¶

• preservice teachers
• students with disabilities in special education settings

Target Learning Goals¶

• effective integration of AI-powered personalized learning
• accessible and inclusive STEM instruction

Related Strategies¶

• CEO Forum recommendations: prepare and certify teachers for technology integration across pre-service, licensure, and in-service career stages
• Provide faculty professional development in adult learning theory and pedagogy for career pathways instruction
• Provide sustained, powerful professional development with professional learning communities for QTEL implementation
• Support faculty AI literacy through needs assessment, targeted training, teaching resources, and communities of practice
• Use the integrated technical–pedagogical limitation framework to inform teacher education, instructional design, and institutional AI policies in undergraduate EFL programs
• Train teachers in prompt engineering, evaluating AI-generated responses, ethical and privacy considerations, and integrating AI outputs into pedagogical decision-making
• Design AI-assisted accessibility tools for personalization, practitioner review, and cultural and contextual specificity

Examples¶

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Key Sources¶

• Holman, Kenneth. (2024). Exploring Fraction Comprehension and Interest in Elementary Education Through AI-Powered Personalized Learning. https://stars.library.ucf.edu/etd2024/32