Date
Publisher
arXiv
Workshop courses designed to foster creativity are gaining popularity.
However, even experienced faculty teams find it challenging to realize a
holistic evaluation that accommodates diverse perspectives. Adequate
deliberation is essential to integrate varied assessments, but faculty often
lack the time for such exchanges. Deriving an average score without discussion
undermines the purpose of a holistic evaluation. Therefore, this paper explores
the use of a Large Language Model (LLM) as a facilitator to integrate diverse
faculty assessments. Scenario-based experiments were conducted to determine if
the LLM could integrate diverse evaluations and explain the underlying
pedagogical theories to faculty. The results were noteworthy, showing that the
LLM can effectively facilitate faculty discussions. Additionally, the LLM
demonstrated the capability to create evaluation criteria by generalizing a
single scenario-based experiment, leveraging its already acquired pedagogical
domain knowledge.
Study at a glance
- Question: Can AI help educators combine diverse evaluation perspectives and explain the underlying teaching theories?
- Setting and data: Not stated in the abstract
- Method: Scenario-based experiments
Why it matters
Even experienced faculty find it challenging to realize holistic evaluations that accommodate diverse perspectives. Deliberation is essential to integrate varied assessments, but faculty often lack time, and deriving average scores without discussion undermines holistic evaluation.
Key findings
- In creative workshop courses, a large language model effectively facilitated faculty discussions to help integrate their diverse student evaluations.
- The model demonstrated the capability to create evaluation criteria by generalizing from a single scenario using its existing pedagogical knowledge.
- Experienced faculty teams struggle to conduct holistic student evaluations that accommodate diverse perspectives because they lack time for adequate deliberation.
Summary drafted with AI from the paper’s abstract and may contain errors. See the full paper for details.
What is the application?
Who is the user?
Who age?
Why use AI?
Study design
