Abstract:

This presentation explores how Artificial Intelligence (AI) can revolutionize education by addressing cognitive diversity through Howard Gardner's Multiple Intelligences (MI) theory. MI theory posits that individuals possess various intelligences, such as linguistic, logical-mathematical, spatial, musical, bodily-kinesthetic, interpersonal, intrapersonal, and naturalistic. Traditional educational approaches often fail to cater to these diverse learning styles (Gardner, 2011), leading to unequal outcomes. AI offers powerful tools to personalize learning, identify student profiles based on their unique MI strengths, and provide tailored resources and learning experiences. For example, AI can analyze student writing to identify strengths in linguistic intelligence or create visual simulations for students with strong spatial intelligence.

The integration of AI presents both opportunities and challenges (AlAli & Wardat, 2024a). AI's capacity to personalize learning is a key focus (Frank, 2024), crucial for addressing cognitive diversity by adapting curriculum and assessment (Forero-Corba & Bennasar, 2024). By leveraging AI, educators can design learning experiences that align with specific MI profiles. Garcia Castro et al. (2024) note AI's impact on teaching methods, further emphasizing AI's potential to cultivate each individual's unique intelligences.

This presentation examines how AI-driven systems can adapt curriculum, assessment, and pedagogy to empower students with varied cognitive profiles, creating a more inclusive and equitable learning environment. Deckker and Sumanasekara (2025) explore the broader role of AI in transforming learning and teaching, supporting the notion that AI can realize the potential of MI theory. Urmeneta and Romero (2024) dive into the creative applications of AI in education, showcasing its potential to go beyond traditional methods. However, ethical considerations, including data privacy and algorithmic bias, are crucial (Caucheteux et al., 2024; Felix & Webb, 2024). Teacher perspectives on AI implementation are also vital and explored in connection to MI (Lee, Davis, & Ryu, 2024; Majji, 2024).

References:
[1] AlAli & Wardat (2024). Opportunities & challenges of integrating generative AI in education. Int J Religion, 5(7), 784-793.
[2] Caucheteux et al. (2024). Students’ perspective on AI in education. In Creative Applications of AI in Education (pp. 101-113). Springer.
[3] Deckker & Sumanasekara (2025). The role of AI in education: Transforming learning & teaching. EPRA IJRD, 10(3), 5.
[4] Felix & Webb (2024). Use of AI in education delivery & assessment. Parliamentary Office of Science & Technology.
[5] Forero-Corba & Bennasar (2024). Techniques & applications of ML & AI in education: A systematic review. RIED, 27(1).
[6] Frank (2024). The Influence of AI on education: Enhancing personalized learning experiences. EasyChair Preprint, 14675.
[7] Garcia Castro et al. (2024). Bibliometric review on teaching methods with AI in education. OJC&MT, 14(2), e202419.
[8] Gardner (2011). Frames of mind: The theory of multiple intelligences. Basic Books.
[9] Lee et al. (2024). Korean in-service teachers’ perceptions of implementing AI education... IJI&ET, 14(2), 214-219.
[10] Majji (2024). Role of artificial intelligence in education. Edumania, 2(01), 33-38.
[11] Urmeneta & Romero (2024). Creative application of AI in education. In Creative Applications of AI in Education (pp. 3-16). Springer.

Keywords:

Artificial Intelligence, AI, Multiple Intelligences, MI Theory, Cognitive Diversity, Personalized Learning, Learning Styles, Student Profiles, Inclusive Education, Equitable Education, Curriculum Adaptation, Assessment, Pedagogy, Ethical Considerations, Data Privacy, Algorithmic Bias Reference.