• Journal of Christian Education in Korea
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• v.77
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• pp.251-276
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• 2024

This study aimed to enhance the educational competencies of instructors and improve the quality of higher education by identifying instructing types, developing an assessment diagnostic tool, and designing a customized faculty training curriculum for each type. To achieve this, a literature review and Delphi research were conducted. The results are summarized as follows: First, instructing types such as 'Star Lecturer', 'Learning Mentor', and 'Designer' were identified through the analysis of previous studies. Second, a diagnostic tool for determining an instructor's type was developed by modifying and enhancing Grasha's Teaching Style Inventory, which is widely used both domestically and internationally. This tool comprises 24 questions, with 8 questions for each type. Third, a curriculum was designed for each instructing type, consisting of common courses necessary for all types and specialized courses tailored to the characteristics of each type. The common courses cover essentials for lesson design, implementation, and evaluation, while the specialized courses cater to the unique needs of each instructing type. Fourth, the developed model, tools, and curriculum underwent validation. A Delphi method was employed with a group of 10 experts, leading to revisions and finalizations based on their feedback. This study has laid the groundwork for instructors to identify their own teaching styles and receive customized training, thereby enhancing their teaching effectiveness and overall educational quality. However, further research is necessary to develop systems and mechanisms for the operationalization of these findings, including incentives for instructors and strategies for disseminating information among participants.

• Journal of Engineering Education Research
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• v.27 no.4
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• pp.3-11
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• 2024

This study classified domestic and international systems by type, presenting their key features and examples, with the aim of outlining future directions for system development and research. AI-based learning assistance systems can be categorized into instructional-learning evaluation types and academic recommendation types, depending on their purpose. Instructional-learning evaluation types measure learners' levels through initial diagnostic assessments, provide customized learning, and offer adaptive feedback visualized based on learners' misconceptions identified through learning data. Academic recommendation types provide personalized academic pathways and a variety of information and functions to assist with overall school life, based on the big data held by schools. Based on these characteristics, future system development should clearly define the development purpose from the planning stage, considering data ethics and stability, and should not only approach from a technological perspective but also sufficiently reflect educational contexts.