TY - GEN
T1 - Preliminary Results from Integrating Chatbots and Low-Code AI in Computer Science Coursework
AU - Kumar, Yulia
AU - Manikandan, Anjana
AU - Li, J. Jenny
AU - Morreale, Patricia
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - The study investigates the application of chatbots and low-code AI tools in advancing Computer Science (CS) education, concentrating on the CS AI Explorations course and the AI for ALL extracurricular programs. It addresses two main research questions: firstly, the impact of chatbots on student growth and engagement in undergraduate research, and secondly, the potential of low-code AI platforms to bridge the gap between theoretical and practical AI skills. Conducted during the 2022-2024 academic years, this research combines case studies and empirical data to evaluate the effectiveness of integrating these technologies into conventional teaching methodologies. The preliminary findings suggest a significant transformative potential for chatbots and low-code AI, offering valuable insights for future educational strategies and developing more dynamic, interactive learning environments. Notably, there was a significant increase in students' involvement in research. Future investigations will elucidate the long-term effects of integrating chatbots and low-code AI.
AB - The study investigates the application of chatbots and low-code AI tools in advancing Computer Science (CS) education, concentrating on the CS AI Explorations course and the AI for ALL extracurricular programs. It addresses two main research questions: firstly, the impact of chatbots on student growth and engagement in undergraduate research, and secondly, the potential of low-code AI platforms to bridge the gap between theoretical and practical AI skills. Conducted during the 2022-2024 academic years, this research combines case studies and empirical data to evaluate the effectiveness of integrating these technologies into conventional teaching methodologies. The preliminary findings suggest a significant transformative potential for chatbots and low-code AI, offering valuable insights for future educational strategies and developing more dynamic, interactive learning environments. Notably, there was a significant increase in students' involvement in research. Future investigations will elucidate the long-term effects of integrating chatbots and low-code AI.
KW - AI Explorations
KW - AI for ALL
KW - Computer Science Education
KW - Low-Code AI
KW - Student Engagement
UR - http://www.scopus.com/inward/record.url?scp=85205564043&partnerID=8YFLogxK
U2 - 10.1109/ISEC61299.2024.10665039
DO - 10.1109/ISEC61299.2024.10665039
M3 - Conference contribution
AN - SCOPUS:85205564043
T3 - 2024 IEEE Integrated STEM Education Conference, ISEC 2024
BT - 2024 IEEE Integrated STEM Education Conference, ISEC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th IEEE Integrated STEM Education Conference, ISEC 2024
Y2 - 9 March 2024
ER -