Work-in-Progress: Immersive and Diversified Artificial Intelligence Education

The world endured drastic change recently. The emerging and innovative technologies will make the societal and industrial transformation exponential in the foreseeable future. A question proposed to universities is how to create innovative educational programs that not only deal with the emerging challenges, but more importantly, lead the digital transformation by inspiring and cultivating the future leaders in the fourth wave of the industrial revolution. The major challenges of AI teaching-learning include the following: a) Managing the extremely diverse backgrounds and experiences of the students within the same cohort; b) Imparting skills in an area that has too many subtopics, many of which are open0ended, and with little hierarchy; and c) The requirement of sufficient practice in solving real-world problems from end-to-end. Addressing these chellenges, the proposed paradigm of Immersive and Diversified Artificial Intelligence Education (IDAIE) will integrate the following educational practices: 1. Pre-assess the capabilities of students to develop a customized study plan to achieve the optimal learning outcomes for each student. 2. Utilize ‘minds-on and hands-on’ loop to reinforce understanding and solidify gained skills. 3. Offer a mini project/hands-on activity every week outside of the classroom to enhance implementation ability. 4. Check their self-reflective pieces and frequently interact with them for open discussion in multiple aspects, i.e., cognitive, psychological, experiential, etc. 5. Encourage the students to self-design open-ended course projects based on their learning curves. 6. Engage community partners and collaborators with the provision of a variety of community/industry projects for students’ participation. This endeavour demonstrated a way to cultivate students’ practical abilities to build a complete artificial intelligence system from scratch. It is noticeable that IDAIE does not mean to diminish the importance of theoretical knowledge learning. It is aimed to incorporate theoretical knowledge learning into practices. This pedagogy can also be applied to AI-related courses and other technical engineering courses.