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Leading in the AI Era: An Interactive Experiential Hands-On Learning Approach for Professionals and Leaders

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Conference

2024 ASEE Annual Conference & Exposition

Location

Portland, Oregon

Publication Date

June 23, 2024

Start Date

June 23, 2024

End Date

July 12, 2024

Conference Session

Computing and Information Technology Division (CIT) Technical Session 3

Tagged Division

Computing and Information Technology Division (CIT)

Permanent URL

https://peer.asee.org/47719

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Paper Authors

biography

Sharifa Alghowinem Massachusetts Institute of Technology Orcid 16x16 orcid.org/0000-0002-9391-0163

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Dr. Alghowinem earned her PhD in multimodal AI from the Australian National University in 2015, following an MSc in Software Engineering at the University of Canberra in 2010 and a BSc in Computer Applications at King Saud University in 2004. Stationed at MIT's Personal Robots Group as a research scientist, she develops AL models that provide insights for enhanced human-robot interaction. With an expertise in multimodal AI, Dr. Sharifa Alghowinem models human behaviors using advanced AI frameworks. Her research focuses on mood and behavior recognition, using speech, gaze, and body movements to detect conditions like depression, suicide risk and deception. With a teaching background at the University of Canberra and a current role as Associate Director of postgraduate programs at Prince Sultan University, she possesses eight years of experience teaching computer science topics, such as AI, to diverse age groups.

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Aikaterini Bagiati Massachusetts Institute of Technology Orcid 16x16 orcid.org/0000-0003-4238-2185

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Aikaterini (Katerina) Bagiati, Ph.D.: Having aquired a Diploma in Electrical Engineering and a Masters Degree in Advanced Computer and Communication Systems at the Aristotle University in Greece, and after having worked as a software engineer, and as a CS educator in both formal and informal settings for 10 years, in January 2008 I decided to leave Greece and get enrolled at the Graduate Program in the pioneer School of Engineering Education at Purdue University.

In August 2011 I acquired my Doctoral Degree in Engineering Education. After serving as a postdoctoral associate within the MIT-SUTD Collaboration for three years, and 7 years as a research scientist, I am currently appointed as a Principal Research Scientist at the MIT Open Learning Office.

My research interests reside in the areas of Early Engineering, Engineering and Technology Curriculum Development and Teacher Training, Online Education, and International Educational Development.

Specialties: Engineering and Technology Education, Instructional Design, STEM Curriculum Development, Digital/Online Education

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Andrés F. Salazar-Gómez Massachusetts Institute of Technology

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Andrés F. Salazar-Gómez is a computational neuroscientist with multidisciplinary training and committed to reshaping higher education in developing countries. Andrés boasts 10+ years of expertise in brain-computer interfaces and human-robot interaction research, with a robust background in biomedical engineering, experimental design, data science and quantitative methods. His focus on evidence-based and policy-informed approaches to educational programs gives him a unique perspective in the field of tertiary, professional and continuing education. Andrés is focused on advocating data-driven insights to enhance educational research and assessment towards accessible high-quality education, for everyone, everywhere.

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Cynthia Breazeal Massachusetts Institute of Technology

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Professor Media Arts and Sciences, MIT Media Lab; Director MIT RAISE Initiative (Responsible AI for Social Empowerment and Education);
MIT Dean for Digital Learning

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Abstract

The pervasive and rapid advancement of Artificial Intelligence (AI) necessitates a fundamental AI understanding for all individuals. AI’s dynamic nature often makes it challenging even for technical people to stay continuously updated on AI, making it especially essential for professionals in leading positions, regardless of their technical background. Understanding AI equips leaders to make knowledgeable decisions about AI’s responsible adoption in their workplace, address its ethical implications, and enable better communication about AI initiatives within the organization. It also demystifies the technology, and ensures leaders can responsibly navigate AI-driven changes.

Most AI-related courses mainly focus on teaching programming languages and handling big data. A closer look at AI adult education reveals gaps and limitations in content suitable for professional adults – e.g in leadership, decision-making, ethics, governance and cultural aspects of organizational change. Furthermore, AI education pedagogy for adult learners,is still understudied. Literature suggests that adults are self-directed, experience-based learners. Therefore, their learning should involve self-planning, experiential learning, collaborative environments, and real-world applications. Notably, current AI education platforms lack sufficient focus on these areas. While efforts are being taken to address these issues, more adult education programs need to be specifically designed for the training needs of leadership in AI.

This research investigates the efficacy and outcomes of an AI education workshop, uniquely designed for large scale organization leaders, employing hands-on projects, collaborative learning, and problem-solving scenarios grounded in real-world applications, that considers technical, ethical, policy and organizational culture dimensions. The workshop incorporates experiential learning methods, case studies, problem-based learning, and group projects. This research also analyzes how adult learners interactively learn, reflect, and apply their AI knowledge to examples drawn from their workplace, while improving their understanding and readiness to implement AI technologies effectively.

Our three-day workshop centered around enriching and engaging learning about AI technologies, ethics, and leadership, featuring topics like supervised learning and bias, AI strategy, and generative AI. Apart from discussions, the workshops incorporated hands-on learning with digital tools, robots, problem-solving scenarios, and a capstone project. Participants were 44 leaders from a large government organization. Their learning was measured through pre- and post-questionnaires on AI leadership, knowledge checks on workshop content, and group interviews post-event.

Results indicated a substantial increase in the participants’ AI knowledge, emphasizing the workshop’s efficacy in enhancing AI literacy, expanding human-AI collaboration, and ethical understanding. The satisfaction survey showed the workshop was enjoyable and substantially improved learners’ understanding of AI and its applications. Learners praised the emphasis on people and ethics in AI and how it inspired them to drive change. They, however, wanted more preparation time and support for understanding content. The capstone project offered an effective hands-on experience that fostered learning and teamwork. Some technical problems were reported, negatively impacting engagement levels. Current research findings are guiding future workshop iterations.

Alghowinem, S., & Bagiati, A., & Salazar-Gómez, A. F., & Breazeal, C. (2024, June), Leading in the AI Era: An Interactive Experiential Hands-On Learning Approach for Professionals and Leaders Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47719

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