TY - GEN
T1 - A Case Study Examines How a New Content Integration Impacts Learning Outcomes with AI Experience Inputs
AU - Ng, Kwan Keung
AU - Li, Shao Fu
AU - Lee, Lap Kei
AU - Luk, Louise
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - This study analyzes the process of integrating artificial intelligence (AI) content into a computer graphics (CG) course for architecture students at a Taiwanese university. The university places a high priority on AI education, and students are required to pass an AI competency test to graduate. The CG course is well-suited for integrating AI components since it relies heavily on visualization and graphics. The study collected both quantitative and qualitative data to assess student reactions and learning outcomes with the new AI content. An end-of-course survey revealed very positive responses. 78–100% of students agreed that the course helped increase their knowledge of AI and awareness of its applications. Further insights were gathered through follow-up focus groups. Students were optimistic about AI learning and did not feel that it detracted from their core architecture studies. However, they suggested slowing down the pace of AI content delivery. While students acknowledged the potential for applying AI in areas like construction visualization, they had a limited view of its broader professional impacts. In summary, this exploratory study suggests that incorporating AI content into an existing technical course was well-received by students and may have improved engagement for struggling learners. However, careful integration is necessary to avoid overwhelming students with too much new material. The study also discusses implications for curriculum design theory and preparing students for AI's professional impacts.
AB - This study analyzes the process of integrating artificial intelligence (AI) content into a computer graphics (CG) course for architecture students at a Taiwanese university. The university places a high priority on AI education, and students are required to pass an AI competency test to graduate. The CG course is well-suited for integrating AI components since it relies heavily on visualization and graphics. The study collected both quantitative and qualitative data to assess student reactions and learning outcomes with the new AI content. An end-of-course survey revealed very positive responses. 78–100% of students agreed that the course helped increase their knowledge of AI and awareness of its applications. Further insights were gathered through follow-up focus groups. Students were optimistic about AI learning and did not feel that it detracted from their core architecture studies. However, they suggested slowing down the pace of AI content delivery. While students acknowledged the potential for applying AI in areas like construction visualization, they had a limited view of its broader professional impacts. In summary, this exploratory study suggests that incorporating AI content into an existing technical course was well-received by students and may have improved engagement for struggling learners. However, careful integration is necessary to avoid overwhelming students with too much new material. The study also discusses implications for curriculum design theory and preparing students for AI's professional impacts.
KW - Artificial intelligence (AI)
KW - Computer graphics (CG)
KW - Computer-aided design (CAD)
KW - Experiential learning
KW - Student learning outcomes
UR - http://www.scopus.com/inward/record.url?scp=85199516018&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-4442-8_10
DO - 10.1007/978-981-97-4442-8_10
M3 - Conference contribution
AN - SCOPUS:85199516018
SN - 9789819744411
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 131
EP - 145
BT - Blended Learning. Intelligent Computing in Education - 17th International Conference on Blended Learning, ICBL 2024, Proceedings
A2 - Ma, Will W. K.
A2 - Li, Chen
A2 - Fan, Chun Wai
A2 - U, Leong Hou
A2 - Lu, Angel
T2 - 17th International Conference on Blended Learning, ICBL 2024
Y2 - 29 July 2024 through 1 August 2024
ER -