1.2. University guidelines on GenAI use
Following ChatGPT’s availability, interest in the educational
implications of GenAI surged. In early 2023, UNESCO released a report on
GenAI in higher education (Miao & Holmes, 2023), outlining several
guidelines. Soon after, many universities formulated their own
guidelines for teachers and students regarding its use (e.g., see
sources [1] to [5] in Table 1). It is common in the existing
guidelines to introduce a general description of GenAI technology along
with cases of tools, usually with an emphasis on ChatGPT. Ideas for
learning tasks that illustrate how GenAI offers opportunities for
enhanced learning tasks (e.g., opportunities for immediate feedback on
written text) are then elaborated on. The guides also alert about risks
and ethical implications, highlighting issues related to bias,
reliability, and privacy. Interestingly, some guides offer strategies to
teachers for adapting assessment tasks to ensure students remain engaged
in active learning, even when utilising these tools. This involves
encouraging connections to personal experiences and reflections, data
collection, and detailed accounts of how GenAI tools have been applied
in completing assignments (e.g., University of Tartu guidelines
for using AI chatbots for teaching and studies , 2023). ).
However, these guidelines typically stop short of offering specific
course policies, leaving these decisions to individual educators.
Generally, they reference the university’s existing or expanded ethics
policies, which build upon traditional plagiarism procedures. However,
these procedures are challenging to apply when GenAI tools are used by
students to replace their expected learning actions (Hernández-Leo,
2023), as detection of GenAI use is problematic (Farrokhnia et al.,
2023). Hence, it may be worthwhile exploring an alternate approach that
can line up with established university academic integrity policies yet
is flexible in its application to allow the individual educator to adapt
it to their course needs and the current technological landscape.
Further, a learning agreement-driven approach can better engage student
decision making and motivational processes, aligning with
self-determination theory – autonomy, competence, relatedness (Deci et
al., 2017) – especially when executed in the context of group work.
This article presents a pilot study involving the use of a learning
agreement for GenAI use that was implemented into a university course
for first-year students in an engineering degree programme during the
2023-24 academic year. The learning agreement approach involved
instructors and students agreeing upon ground rules for AI use at the
onset of a course via a multi-page online form. The motivation of the
work was to explore whether a learning agreement can be an acceptable
tool for governing student use of GenAI in university coursework. As
such, the research questions being explored are: R1. Would first-year
students be accepting of a course learning agreement on GenAI use? R2.
Would students adhere to the terms of the learning agreement in their
group assignment? R3. After experiencing a course learning agreement,
how would students improve on its use? To address these questions, the
article contributes the following: (a) A learning agreement for GenAI
use in university courses and literature supporting its terms; (b) A
description of modifications made to the course design to account for
student use of GenAI and the terms of the learning agreement; (c)
Results of a pre-post study exploring changes in student awareness and
use of GenAI tools, beliefs related to how such tools should be used in
university learning, and thoughts on a learning agreement for GenAI use
approach; (d) An analysis of student adherence to terms of the learning
agreement on a key course assignment; and (e) Findings from an analysis
of student suggestions to improve the learning agreement approach
adopted.