TY - GEN
T1 - Grading Generative AI-based Assignments Using a 3R Framework
AU - Chan, Henry C.B.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/11
Y1 - 2023/11
N2 - With the advent of generative artificial intelligence (GenAI), there is a strong need to revisit the grading or assessment mechanism. In this paper, we present a 3R framework to facilitate the grading of GenAI-based assignments. Basically, there are three essential components: Report, Revise and Reflect. Students should report on how they use GenAI tool(s). They should also revise its output by providing their own input or contributions. Last but not least, they should provide a learning reflection. We also present a 3R rubric for evaluation purposes and propose a GPT formula for determining an effective grade. For illustration purposes, we discuss two cases, covering essay assignments and programming assignments. Furthermore, to evaluate the 3R framework from the student perspective, we present and discuss student survey results. The 3R framework can provide the basis for further research study as well.
AB - With the advent of generative artificial intelligence (GenAI), there is a strong need to revisit the grading or assessment mechanism. In this paper, we present a 3R framework to facilitate the grading of GenAI-based assignments. Basically, there are three essential components: Report, Revise and Reflect. Students should report on how they use GenAI tool(s). They should also revise its output by providing their own input or contributions. Last but not least, they should provide a learning reflection. We also present a 3R rubric for evaluation purposes and propose a GPT formula for determining an effective grade. For illustration purposes, we discuss two cases, covering essay assignments and programming assignments. Furthermore, to evaluate the 3R framework from the student perspective, we present and discuss student survey results. The 3R framework can provide the basis for further research study as well.
KW - assessment
KW - ChatGPT
KW - generative AI
UR - http://www.scopus.com/inward/record.url?scp=85184989558&partnerID=8YFLogxK
U2 - 10.1109/TALE56641.2023.10398408
DO - 10.1109/TALE56641.2023.10398408
M3 - Conference article published in proceeding or book
AN - SCOPUS:85184989558
T3 - 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings
SP - 1
EP - 14
BT - 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023 - Conference Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Teaching, Assessment and Learning for Engineering, TALE 2023
Y2 - 28 November 2023 through 1 December 2023
ER -