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Personalized learning, which customizes content and instructional sequences to account for differences in ability, experience, and sociocultural backgrounds, holds great promise for transforming education. This transformation is increasingly driven by significant advancements in Artificial Intelligence (AI). AI enables detailed analysis and reporting of learners' performance data, paving the way for the development of intelligent adaptive learning systems that offer personalized feedback aligned with each learner’s unique needs and progress. In parallel, immersive technologies are playing a pivotal role in enhancing educational experiences. Technologies such as Virtual Reality (VR) and Augmented Reality (AR) create engaging, interactive environments that deepen learners' understanding and retention of complex concepts. Dr. Vassigh's presentation explores the integration of AI and VR in education, illustrated through a case study from an ongoing project. The talk will highlight the refinement of learning processes through these technologies and demonstrate how they can impact learner engagement and performance.
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Enhancing Data Science Courses Pedagogy through GIFT-Enabled Adaptive Learning Pathways
Over the past decade, the educational landscape has experienced a surge of online learning and instruc-tional platforms (Liu et al., 2020). This remarkable surge can be attributed to a confluence of factors, including the rising demand for higher education opportunities, the shortage of available teaching staff, and the rapid advancements in information technology and artificial intelligence capabilities. Artificial Intelligence (AI) remained a niche area of research with limited practical applications in education for over half a century (Bhutoria, 2022; Chen et al., 2020; Roll & Wylie, 2016) from 1950 to 2010. Howev-er, in recent years, the advent of Big Data and advancements in computing power have propelled AI into the educational mainstream (Alam, 2021; Chen et al., 2020; Hwang et al., 2020). Today, the rise of machine learning, deep learning, automation, together with advances in big data analysis has sparked novel perspectives and explorations around the potential of enhancing personalized learning, a long-term educational vision of technology-enhanced course options to meet student needs (Grant & Basye, 2014). Fostering personalized learning necessitates the development of digital learning environments that dynamically adapt to individual learners' knowledge, prior experiences, and interests, while effectively and efficiently guiding them towards achieving desired learning outcomes (Spector, 2014, 2016). AI-powered technologies have made it possible to analyze data generated by learners and provide instruc-tion that matches their learning performance. Through learning analytics and data mining techniques, large datasets collected are analyzed and processed to uncover learners' unique learning characteristics, often referred to as learner profiling (Tzouveli et al., 2008). Subsequently, leveraging artificial intelli-gence algorithms, the learning content is tailored, and personalized learning paths are designed to align with each learner's identified needs and preferences, thereby facilitating personalized learning experienc-es.
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- Award ID(s):
- 2142514
- PAR ID:
- 10531691
- Editor(s):
- Sinatra, Anne; Goldberg, Benjamin
- Publisher / Repository:
- GIFTSymp 12
- Date Published:
- Subject(s) / Keyword(s):
- Generalized Intelligence Framework for Tutoring Adaptive Distributive Learning Learning Assessment Data Science Education
- Format(s):
- Medium: X
- Location:
- Orlando, Florida
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
