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\begin{document}

\title{A Learning Agreement for Generative AI Use in University Courses: A
Pilot Study}



\author[1]{Marc Beardsley}%
\author[1]{Ishari Amarasinghe}%
\author[1]{Emily Theophilou}%
\author[1]{Milica Vujovic}%
\author[1]{Davinia Hernández-Leo}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
The rapidly evolving landscape of Generative AI (GenAI) tools
necessitate continuous vigilance and adaptation by educators. This
dynamism requires stakeholders to stay up to date with developments to
address emerging issues effectively, creating complexity in managing the
responsible and ethical use of GenAI. This paper presents a pilot study
involving the use of student learning agreements for governing GenAI use
in a first-year engineering degree course. The learning agreement
contains ethical and social considerations students agree to make if
they decide to use GenAI in the course. As part of the pilot study,
pre-post surveys and student artefacts -- a group assignment adapted to
accommodate GenAI use -- were analysed. Results show that the vast
majority of students were in favour of the learning agreement approach
both at the start and upon completion of the course. However, 7 of 17
groups did not use GenAI in their assignment. Of the 10 groups that did,
only 1 acknowledged GenAI limitations in adherence with the learning
agreement. A thematic analysis of student suggestions for improving the
learning agreement approach include suggestions for making the agreement
easier to understand and adhere to (e.g., providing specific examples,
reengaging with the agreement during the course). Overall, findings
suggest that learning agreements have the potential to offer an
interface through which student decision-making can be supported and
interactions among students, educators, researchers, and policy makers
related to the ethical and societal challenges of GenAI can take place.%
\end{abstract}%



\sloppy


\section*{1. Introduction}

{\label{introduction}}

Generative AI (GenAI) is disrupting education. GenAI refers to a broader
set of technologies that can generate new and unique content, in various
formats such as text, images, audio, code, text, simulations, 3D
objects, and videos (Moorhouse et al., 2023). The concept of GenAI is
closely tied to Large Language Models (LLMs), which are explicitly
designed to facilitate the creation of text-based materials. The advent
of free, relatively easy-to-use, online LLM based conversational
interfaces such as ChatGPT has quickly transformed students' use of AI
in education. Specifically, tools such as ChatGPT can automatically
generate text in response to a human prompt and have raised implications
for assessments (Sharples, 2022).~As a result, many educators worry
about new forms of academic dishonesty as students can simply copy and
paste content generated by these technologies potentially engaging in
uncredited use of AI-generated text, misrepresenting one's abilities,
and neglecting essential learning processes (Padillah, 2023;
Mohammadkarimi, 2023; Habib et al., 2024). Hence, use of GenAI by
students can undermine principles of fairness in education and diminish
the value of personal achievements (Padillah, 2023).

Ethical concerns related to the use of GenAI such as bias,
accessibility, and privacy have also been raised (Sabzalieva \&
Valentini, 2023). The issue of bias in produced content is a significant
ethical challenge (Ferrer et al., 2021; Zhou, et al., 2023) as the
models learn from data, and if that data reflects societal biases, the
generated content can introduce or reinforce these biases in its outputs
(Akter et al., 2021). Another concern is the potential for inequality
arising from varying resources and prior knowledge required to access
and utilise advanced functionalities of AI language models effectively
(Ng et al., 2021). The digital divide, already a societal challenge,
could be worsened as those with more resources and technical know-how
could disproportionately benefit from these technologies. There are also
concerns related to individual privacy and intellectual property rights.
Using AI language models involves processing vast amounts of user data
(Gupta et al., 2023) and copyrighted materials -- but often without
adequate notification or consent (Lucchi, 2023). The need for data input
to train and fine-tune these models presents a risk to individual
privacy, with questions arising about the security and responsible
handling of sensitive information (Wu et al., 2023) by the private
enterprises offering public access to GenAI technologies.

In response, several universities attempted to temporarily restrict or
ban access to GenAI tools (Moorhouse et al., 2023). However, concerns
have been raised about the impact of restricting access in academic
contexts, considering the impact on students' AI literacy and their
readiness for a society increasingly powered by artificial intelligence
(Chiu, 2024; Chiu 2023). Further, the disruptiveness of GenAI does not
rest solely with its potential to interfere with student learning but
also in its promise for enhancing student learning and the effectiveness
of educators. A comprehensive guide on using ChatGPT and Artificial
Intelligence in higher education published by UNESCO explains how AI
tools used both by students and educators can enhance learning
experiences. The authors of the guide present diverse roles AI can
fulfil such as those of a~personal tutor, co-designer, and motivator
(Sabzalieva \& Valentini, 2023). Furthermore, Miao (2021) highlights the
importance of collaborative intelligence between humans and machines and
describes four main areas in which there are emerging and potential
applications of AI in education: (1) Education management and delivery,
(2) Learning and assessment, (3) Empowering teachers and improving
teaching, and (4) Promoting lifelong learning. Recognizing opportunities
for disruption becomes essential for not only improving the
effectiveness of education but also for preparing students with the
skills required for employment in a world increasingly influenced by AI
integration (Alekseeva, et al, 2021).

The rapidly evolving landscape of GenAI tools, with frequent releases of
new tools and improvements to existing ones, necessitate continuous
vigilance and adaptation (Gill et al., 2022). This dynamism requires
stakeholders to stay up to date with developments to address emerging
issues effectively, creating additional complexity in managing the
responsible and ethical use of GenAI. As society faces these concerns,
there is a need to find a balance that allows the benefits of advanced
AI language models to be received while addressing the associated
ethical and societal challenges. Considering the opportunities,
concerns, and the immediacy of the disruption to university education, a
process is needed that can gather insights into how AI is being used,
assess whether such uses are effective or not, and bring stakeholders
together to define acceptable uses of AI in education (Beardsley et al.,
2024). Student learning agreements may offer a flexible, student-centred
approach that gathers informative data while providing opportunities for
students to become more thoughtful in their decision making related to
the use of innovative technologies such as GenAI.

\textbf{1.1. Learning agreements}

Learning agreements (LA), learning plans or student contracts, strive to
be transformative educational tools that aim to place students in
control of their learning journeys (Clear et al., 2016). LAs provide
incremental steps through which students assume ownership of their
learning, fostering a sense of accountability, community belonging, and
shared expectations with their teachers (Nielsen et al., 2022).
Interchangeable with learning partnerships, LAs can be used to shape
sustainable learning environments and partnerships (Wallis, 2013).
Predominantly appearing in medical education literature, LAs are often
integrated into time-schedule agreements for medical students (Ahuja et
al., 2013). The aim is to have students formulate specific learning
agreements early in their training placements and continuously review
progress throughout their placement periods (McKee, 2008). LAs have also
been used in workplace learning, particularly in work-based higher
education settings. Studies, mainly concentrated in the UK, highlight
LAs role in fostering socially responsible approaches to professional
development within workplace settings (Wallis, 2013). LAs or contracts
for internships, particularly in the context of sociology students'
internships (Parilla \& Hesser, 1998) and for facilitating student
mobility and exchange programs (Bustamante et al., 2010) demonstrate the
adaptability and versatility of LAs across various educational and
professional contexts. The multifaceted application of learning
agreements spans medical education, workplace learning, mobility
programs, and internships, showcasing their role in empowering
student-centred learning, fostering partnerships, and ensuring the
alignment of educational objectives with diverse learning contexts.

\textbf{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., \emph{University of Tartu guidelines
for using AI chatbots for teaching and studies} , \emph{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 (Hern\selectlanguage{ngerman}ández-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.

\section*{2. Methodology}

{\label{methodology}}

This study follows a design-based research methodology (DBR) (Anderson
\& Shattuck, 2012) and applies a mixed methods design. DBR has been
selected as there are plans to iterate upon the design of the learning
agreement in subsequent academic years.

\textbf{2.1. Participants}

Participants were engineering degree program students enrolled in a
first-year general studies course at a university in Spain that
consented to sharing their data (female = 26, male = 56, other = 01,
prefer not to say = 01, M-age = 18.17, SD-age =1.37). The general
studies course, Introduction to Information and Communication Technology
(ITIC), serves to introduce the university and engineering studies.
Topics include: an overview of the study program, transversal
competences at the level of higher education (learning to learn,
teamwork, written and oral communication), academic integrity, an
overview of the ICT sector, and codes of ethics and professional conduct
in engineering. The course includes plenary sessions that introduce
content and include interactive discussion led by professors, and
seminar sessions for more active student learning. Seminars activities
vary from short individual and group tasks to debates, and the
elaboration and presentation of a group written article about the future
(and ethical implications) of a technology of their choice. A revision
of the course was done in 2023-2024 to account for the availability of
GenAI tools.

\textbf{2.1. Pre-post survey}

As part of this study, participants completed two online surveys (Google
Form) while in class. The pre-survey was completed during the first week
of the first trimester of the 2023-24 academic year. The survey
contained 7 items rated on a 5-point Likert scale (1 = Strongly
disagree, 5 = Strongly agree) related to awareness, use, and beliefs
about GenAI and learning agreements; and 6 related open-ended questions.
After 8 weeks, in the latter half of the final class of the course,
students completed a post-survey. The post-survey contained 6 items
rated on a 5-point Likert scale related to awareness, use, and beliefs
about GenAI, 4 items rated on a 5-point Likert scale related to the
usefulness of the learning agreement, 1 required open-ended question on
improving the learning agreement, and 2 optional open-ended questions.

\textbf{2.2. Learning agreement for GenAI use}

The content for the learning agreement came from a cursory review of
university policies on GenAI use and scientific literature on AI use in
education. Five key themes were identified to include in the learning
agreement. Table 1 presents a summary of the sources relating to each
theme. The learning agreement was presented to students as a Google Form
with 9 sections. The first section presented the purpose of the learning
agreement as a participatory process, \emph{We want to help you gain
experience in using innovative technologies (that can benefit your
future career) in a responsible manner -- and for you to help us improve
our definition of what responsible use means.} The second and third
sections provided instructions for completing the form and collecting
student information -- and included the following text to describe the
intention of the agreement:

\emph{Students are encouraged to use AI tools to contribute to their
learning in the course (i.e., as learning aids or to help produce
designated assignments). However, students using such tools are only
permitted to do so in accordance with the terms outlined in this form. A
failure to do so may result in a violation of university rules for
academic integrity and risk suspension/expulsion from the university.}

Sections four to eight contained the key themes as shown in Table 1 and
converted them into agreement items (i.e., choice selections to be made
by students). For example, item 1 relating to the first theme presents a
checkbox with the text, \emph{I understand that in this ITIC course AI
tools cannot be used during assessment and opinion survey tasks unless
specified otherwise by your instructor} .

\textbf{Table 1}

\emph{Themes included in the Student Learning Agreement for Governing
GenAI Use in University Courses}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Theme} & \textbf{Description} & \textbf{Sources}\tabularnewline
1. Not for assessments & Assessments help us understand whether students
have learned course material. Opinion surveys help us understand your
views on topics. The use of AI for assessment and opinion survey tasks
can defeat the purpose of such tasks. However, AI tools may be useful
when gathering information to support your studying for these
assessments (prior to starting the assessment tasks). If you are not
sure whether AI tools can be used for a specific assignment, please ask
your instructor. & {[}1{]}{[}2{]}{[}3{]}{[}7{]}\tabularnewline
2. Declared and described use & AI tools can be used to assist in the
completion of key course assignments (article, debate, presentation) and
other assignments explicitly designated by your instructors. However,
any AI tool usage must be declared and described in your submitted
assignments. & {[}1{]}{[}2{]}{[}5{]}
{[}8{]}{[}9{]}{[}10{]}\tabularnewline
3. Appropriate citations & Any content (text, image, data) from an AI
tool must be cited according to APA standards. This applies to in-text
citations and references (e.g. paraphrasing, quoting, or incorporating
any content from an AI tool into your own work). For example, if you
copy and paste AI-generated text directly to your assignment, you must
add quotations around the text, ''the influence of beavers on their
environment can be both constructive and destructive, contingent on the
particular context'' (OpenAI, 2023). & {[}2{]}{[}4{]}{[}5{]}
{[}6{]}{[}11{]}\tabularnewline
4. Responsible use & AI tools are known to have limitations and may have
problematic ethical origins and/or intentions. 1) Biases: Biases in
datasets used to train AI can perpetuate social inequalities and
injustices. 2) Privacy: Datasets used to train AI may violate data
privacy and intellectual property rights. AI tools may also collect,
store, and use user data for undisclosed purposes. 3) Misinformation: AI
tools are known to fabricate facts and can be used to spread
disinformation. 4) Power and responsibility: Popular AI tools are closed
(not transparent), proprietary models that consume massive amounts of
resources (natural, financial) and have the power to shape public
opinion. & {[}2{]} {[}4{]}{[}7{]} {[}11{]}{[}12{]} {[}13{]}{[}14{]}
{[}15{]}\tabularnewline
Informed use & Usage of AI tools involves the sharing of personal data
and, possibly, intellectual property with third parties. Confidential
information should never be inputted into an AI tool as entered content
may become part of the tool's dataset and appear in response to other
prompts. & {[}2{]}{[}4{]}{[}7{]} {[}11{]}{[}12{]}
{[}15{]}\tabularnewline
\bottomrule
\end{longtable}

\emph{Note. References for sources: {[}1{]} Generative AI Policy
Guidance, n.d.; {[}2{]} ChatGPT and Generative AI in the Classroom --
Office of the Vice-Provost, Innovations in Undergraduate Education,
n.d.; {[}3{]}} \emph{UCLb, 2023; {[}4{]}} \emph{Guidance for the Use of
Generative AI -- UCLA Center for the Advancement of Teaching, n.d.;
{[}5{]} University of Tartu guidelines for using AI chatbots for
teaching and studies. (2023, April 28). {[}6{]} LibGuides: References,
Citations and Avoiding Plagiarism: Acknowledging and Referencing AI,
n.d.; {[}7{]} Sabzalieva \& Valentini, 2023; {[}8{]}} \emph{Gatrell, et
al, 2024; {[}9{]} Tang, et al, 2023; {[}10{]}} \emph{Flanagin, et al,
2023; {[}11{]} McAdoo, T., n.d; {[}12{]} Bozkurt et al., 2023; {[}13{]}
Miao \& Holmes, 2021; {[}14{]}} \emph{Hryciw, et al, 2023;
{[}15{]}~Akgun \& Greenhow, 2022.}

\textbf{2.3. Course design modifications for GenAI use}

Considering the availability of GenAI tools, some sessions, assignments,
and related grading criteria in the course were redesigned to offer room
for accommodating the opportunities of GenAI while also attempting to
mitigate its risks. Firstly, a plenary session dedicated to the code of
ethics of the university and academic integrity was redesigned to
include an introduction to GenAI. The session provided insights into how
it operates, fostered a discussion on the opportunities it presents in
enhancing learning, while addressing challenges and risks associated
with its use. Secondly, the assignment description and related grading
criteria for the group written article was modified to require an
appendix in which groups were to a) Declare databases (e.g., ACM Digital
Library) and tools used (including any AI tools) to assist the
completion of the article, with a description of how they were used. If
AI tools were not used, groups were to include a reflection explaining
the reasons. Additionally, groups were to explain how feedback from
peers, their tutor, and (if apply) AI tools were considered. Finally,
the content of a seminar session to scaffold the elaboration of the
group written article was redesigned. During the session tutors
introduced students to GenAI tools and prompts to generate the feedback,
e.g. ChatGPT to check coherence, Grammarly to check grammar, Elicit to
search for additional evidence. Groups also received feedback from
tutors and peers -- and were given the choice of whether to take
advantage of GenAI tools for improving their drafts.

\textbf{2.4. Procedure and data analysis}

During the first week of the trimester, students completed the online
pre-survey and learning agreement form prior to attending a plenary
lecture that introduced GenAI and Academic Integrity. The terms of the
learning agreement were discussed in the lecture and students were able
to change their learning agreement form responses following the lecture.
In week 6, students were encouraged to use GenAI during a 2-hour seminar
class to receive personalised feedback on the key assignment (group
written article). In week 9, the key assignment was delivered by
students and student groups made formal presentations of their group
articles. Following the presentations, students completed the
post-survey. Python was used to analyse the quantitative data and
thematic analysis was used for qualitative data (O'Connor \& Joffe,
2020).

\section*{3. Results}

{\label{results}}

\textbf{3.1. Course artefacts (adherence)}

A requirement of the group written article was to include in the
appendices of the article a description of AI tools used. The
requirement was written as follows, \emph{Declaration of the databases
and tools used (including any AI tools), when applied to assist the
completion of the article, with a description of how they were used. If
AI tools have not been used, include a reflection explaining the
reasons.} Further, items 2-4 of the learning agreement required students
to include specific information in their reporting of AI tool use.

Item 2: \emph{The appendix must (1) document what tool(s) were used, (2)
how the tools were used (i.e., prompts used), and (3) how the results
from the AI tools were incorporated into the submitted work.}Item 3:
\emph{Any content (text, image, data) from an AI tool must be cited
according to APA standards
(https://libguides.mcmaster.ca/cite-gen-ai/apa). This applies to in-text
citations and references (i.e. paraphrasing, quoting, or incorporating
any content from an AI tool into your own work).~}

Item 4: \emph{I am obliged to use AI tools in a responsible manner. This
means that as part of the appendix I submit with course assignments, I
must explicitly comment on actions taken to mitigate and/or acknowledge
the known limitations of the AI tools.}

In the course a total of 17 group written articles were submitted. To
rate compliance, two raters reviewed the article appendices and rated
them for compliance using the following 4-point scale: `nothing' (0)
with no text relating to the specific item, `incomplete' (1) with text
relating to the specific item but addressing less than half of what was
requested, `partially complete' (2) with text that addressed more than
half but not all of what was requested, and `complete' (3) the text
fully addressed what was requested in the learning agreement item. In
total, 51 ratings were performed and there was high agreement with
initial ratings (86.27\%). Disagreements were resolved through
discussion. Table 2 shows the results of group written article adherence
to the items in the learning agreement. Results suggest that students
adhered to the article requirement but not the learning agreement terms.
Although, it should be noted that a score of zero was assigned when no
specific text addressed the item in the student artefact\textbf{.} The
zero score does not distinguish the cases in which students did not feel
it was necessary to add specific text. For example, 7 of the 17 groups
declared that they did not use GenAI to support their work. Hence, it is
possible that they did not feel obliged to elaborate on Item 3
(Appropriate citations) and Item 4 (Responsible usage). Only 1 of the 10
groups that did declare use of GenAI provided prompts used via a link to
a ChatGPT thread, and none of these groups cited OpenAI in their text or
as part of their listed references. Also, only 1 of the 10 groups
explicitly acknowledged GenAI limitations, including biases in the
training data, the reliability of AI-generated content, and fairness in
AI-powered writing systems.

\textbf{Table 2}\emph{}

\emph{Adherence to the Learning Agreement in Student Artefacts (Group
Article Assignment)}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
~ & Item 2: Declaration & Item 3: Citation & Item 4: Responsible
Use\tabularnewline
Nothing (0) & 0 & 100\% (17) & 70.6\% (12)~\tabularnewline
Incomplete (1){} & 17.6\% (3){} & 0{} & 11.8\% (2)~\tabularnewline
Partially complete (2) & 41.2\% (7) & 0 & 5.9\% (1)~\tabularnewline
Complete (3){} & 41.2\% (7){} & 0{} & 11.8\% (2)~\tabularnewline
\bottomrule
\end{longtable}

In reviewing reasons provided by the 7 groups for not using GenAI, one
group mentioned not using GenAI for content generation due to its
perceived deficiency in understanding human values thereby hindering its
utility for the assignment. Specifically, they stated: \emph{''The
ethical implications of AI require a deep understanding of human values
and moral complexities. AI lacks the ability to make moral decisions,
empathise with others, and understand context. A human perspective is
essential for understanding complex social and ethical issues.''} A
second group expressed concerns that relying on AI for writing
assistance would compromise essential skills, such as writing
proficiency. Specifically, they stated: \emph{''Developing a set of
basic skills independent of AI technology is crucial. Writing is a
valuable skill that should be developed and maintained.''} The other 5
groups did not provide reasons.

\textbf{3.2. Pre-post survey findings}

The final sample size of the pre-post surveys was 53. This is a decrease
from the initial sample size of 73 due to students not completing both
pre- and post-surveys. In this sample, the mean age of the students was
17.98 years old, and the SD was 0.91. The majority of the students were
Spanish (94.34\%) with a small number of students coming from Africa (1
student), South America (1 student) and Asia (1 student). The final
sample is composed of 60.4\% male students and 39.6\% female students.
Students completed these questionnaires at the start and end of the
course. During the course they were encouraged to utilise GenAI tools to
help them with their work. The analysis of their responses (see Figure
1) reveals notable shifts in attitudes towards GenAI, reflecting the
impact of hands-on experience and exposure during the course. Results
show that students reported a significant increase in
their\emph{familiarity} with GenAI tools (mPre = 3.09, SD = 0.9; mPost =
3.91, SD = 0.88, p \textless{} 0.05). Students reported a significant
increase in their \emph{use} of GenAI tools to assist with their
schoolwork (mPre = 2.85, SD = 1.3, mPost = 3.57, SD = 1.2, p \textless{}
0.05). A significant increase in the belief that students should have
the \emph{option to use GenAI} tools in university courses was
identified (mPre = 3.60, SD = 0.9; mPost = 4.13, SD = 0.81, p
\textless{} 0.05). However, there was no significant change in the
belief that students should have the \emph{option to opt out of using
GenAI} in class (mPre = 3.53, SD= 1.14; mPost = 3.92, SD = 1.02, p
\textgreater{} 0.05). A significant increase was observed
in\emph{students' plans to use GenAI} to support their learning in
university courses (mPre = 3.13, SD = 0.94; mPost = 3.74, SD = 0.94, p
\textless{} 0.05). Students did not exhibit a significant change in
their belief that \emph{grading criteria should differ} for students
using AI tools versus those who do not (mPre = 2.43, SD = 1.26; mPost =
2.53, SD = 1.19, p \textgreater{} 0.05). Lastly, students expressed a
significant decrease in the belief that \emph{each course should have a
learning agreement} related to the use of GenAI (mPre = 4.45, SD = 0.77;
mPost = 4.00, SD = 0.85, p \textless{} 0.05). Although, student ratings
still averaged 4 out of 5 on a Likert scale in the post survey
suggesting continued support for the learning agreement approach.

\textbf{Figure 1}

\emph{Results of the 7 pre-post closed questions (5-point Likert scale)
on Generative AI}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.98\columnwidth]{figures/Figure1/Figure1}

\end{center}
\end{figure}

\emph{}\textbf{3.3. Thematic analysis of student suggestions}

Students' responses to ``How can the learning agreement and its use in
this course be made more useful for future years?'' were analysed
following a thematic approach. Two authors of the study coded the text
responses following a codebook (Table 3). In the following we report the
inter-rater agreement for each code individually using Cohen's Kappa at
the end of the first round of coding across nine codes/categories.
e.g.,\emph{Improve clarity} k = 0.562 (Moderate agreement); \emph{More
class discussion} k = 0.586 (Moderate agreement); \emph{No
modifications} k = 0 (No agreement); \emph{More student-centred} k = 0
(No agreement);\emph{More strictly enforced} k = 0.382 (Fair agreement);
\emph{Adapt to the context} k = 1.00 (Almost perfect agreement);
\emph{Keep it updated}k = 0.465 (Moderate agreement); Other k = 1.00
(Almost perfect agreement); \emph{Easier to read} k = 0.633 (Almost
perfect agreement). As it can be seen, \emph{No modifications} and
\emph{More student-centred} codes did not reach agreement between the
two coders at the end of the first round of coding. \emph{Improve
clarity, More class discussion, Keep it updated} codes reached Moderate
agreement.\emph{More strictly enforced} code reached a Fair agreement.
All disagreements were thoroughly discussed and resolved during a second
round of coding to reach a consensus.

Results of the thematic analysis reveal that a relatively high number of
students indicated that no modifications are required (16.67\%), a few
students did not provide an opinion (3.33\%), some felt that strictly
enforcing the terms of the existing agreement would improve it (8.33\%),
while others mentioned the need to keep adapting the learning agreement
to the context of specific courses and current societal views on GenAI
(5\%). 51.66\% of suggested improvements related to helping students
better understand and adhere to the learning agreement. Examples include
improving the clarity (28.33\%) of the learning agreement with specific
examples, providing additional time in class to discuss it in more
detail (18.33\%), incorporating regular reviews and updates to the
learning agreement to facilitate adherence (3.33\%), and making the text
more concise to increase readability (1.67\%). 11.67\% of suggested
improvements related to better supporting student autonomy and
participation in defining and applying the learning agreement. In other
words, they suggest making the learning agreement more student-centred
rather than teacher-centred (11.67\%).

\textbf{Table 3}

\emph{Coding Scheme used to analyse students' post-activity
questionnaire responses}\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Code\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Explanation\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Count\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Improve clarity\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Provide specific examples of accepted and not accepted uses as well as
limitations. E.g., \emph{Although I think it is useful enough, maybe the
way to improve it would be by making this learning agreement more
specific. Specifying on what is correct and what is not. So students can
avoid misunderstandings.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
17 (28.33\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
More class discussion\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Allocate more time to discuss this topic in class. E.g., \emph{Maybe it
should be talked a little bit more in class and put more examples to
make it more useful for students.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
11 (18.33\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
No modifications\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
No modifications are needed to the existing learning agreement. E.g.,
\emph{I believe it was very well explained, and I don't think it should
be modified.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
10 (16.67\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
More student-centred\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Make it more student-centred by shifting the focus towards individual
student goals and needs. E.g., \emph{It could improve by giving students
more margin to decide how they want to do their work to appeal to their
creativity.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
7 (11.67\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
More strictly enforced\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Enforce the terms of the agreement. E.g., \emph{I think that if
everything is clearly stated and sanctions are imposed more strictly it
will be more useful for everyone.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
5 (8.33\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Adapt to the context\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Update the learning agreement between courses so that the ''new''
learning agreement has been adapted to changes in technology, society,
course goals, etc. E.g., \emph{Adapting to the situation of the moment
and to what you're applying it; it's impossible to have a learning
agreement for everything, it must have some differences between them so
they are better applied.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
3 (5\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Facilitate adherence\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Come back to the learning agreement regularly in the course to help
students update it and/or ensure they are adhering with it. E.g.,
\emph{What it can be done is to incorporate a system for regular reviews
and updates of the learning agreement. This allows us to track our
progress, make necessary adjustments, and ensure we are on the right
path towards achieving our goals.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
2 (3.33\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Other\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
No suggestions given. E.g., \emph{I don't know}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
2 (3.33\%)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Improve readability\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Make the text easier and quicker to read. E.g., \emph{From my point of
view, it is fine and well-clarified. However, it could be easier and
quicker to read, it could go more directly to the main point.}\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
1 (1.67\%)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\section*{4. Discussion}

{\label{discussion}}

A recent publication (Author, 2024) presented an analysis of the
pre-survey responses. Results show inequality in the level of
familiarity with and prior use of GenAI by students. Furthermore,
students expressed a desire for autonomy in being able to choose whether
to use GenAI or not, with the vast majority agreeing that students
should retain an option to opt out of its use. Finally, most were
uncertain whether they were going to use GenAI or not in the course,
perhaps due to a lack of certainty over what types of uses were
permitted. In the end, 58.8\% of the groups declared using GenAI in the
group written article. A recent study of 6,300 university students in
Germany found that 63.4\% of students reported using AI-based tools for
their studies (von Garrel \& Mayer, 2023) which is consistent with our
findings when considering that the rate in our study was not for
individual use but for use within a specific group assignment. While
two-fifths of the groups declared not using GenAI, overall, students
reported a significant increase in their familiarity with GenAI tools,
their use of GenAI tools to assist with their schoolwork, and in their
plans to use GenAI to support their future learning at the university.
This aligns with findings from previous work, where students acknowledge
ChatGPT, as a valuable complementary learning resource and describe it
as helpful for learning (S\selectlanguage{ngerman}ánchez-Reina et al., 2024; Shoufan,
2023).~Also, by the end of the course, no significant changes were found
in student beliefs related to students having the option to opt out of
using GenAI in class, nor to beliefs that grading criteria should differ
for students using AI tools versus those who do not.

In relation to the first research question, \emph{R1. Would first-year
students be accepting of a course learning agreement on GenAI
use?,}pre-survey results show the vast majority of students supported
the use of a course learning agreement for governing GenAI in higher
education (\emph{M =} 4.45\emph{, SD =} .77). Most utterances (86.1\%)
related to either preventing students from being unfairly disadvantaged
or helping students learn to use AI properly as reported by Beardsley et
al. (2024) which provide insights into student expectations of the
learning agreement. At the end of the course, student support had
decreased significantly but still averaged 4 out of 5 on a Likert scale
(\emph{M =} 4.00\emph{, SD =} .85), suggesting continued support for the
learning agreement approach but, perhaps, a failure to meet student
expectations. In relation to the second research question, \emph{R2.
Would students adhere to the terms of the learning agreement in their
group assignment?} , all student groups did make a declaration involving
technologies used, yet none felt the need to cite their use of GenAI
tools, and only 1 of the 10 groups that did declare use of GenAI
acknowledged its limitations as agreed upon in the learning agreement.
Hence, it appears students did not adhere to all items in the learning
agreement -- especially items 3 and 4. Findings related to the third
research question, \emph{R3. After experiencing a course learning
agreement, how would students improve on its use?,} provide insight into
possible reasons for low adherence for these items. The lack of specific
examples of what is not permitted, class time for further discussion of
issues related to the learning agreement, and opportunities to re-engage
with the learning agreement may have contributed to the lower post
rating -- as these were suggestions for improvement. Additionally,
several utterances related to student motivation to adhere to the
learning agreement with some (8.33\%) suggesting a stricter enforcement
of the learning agreement terms being needed while others (11.67\%)
suggesting the approach should be more participative and supportive of
student autonomy. The latter aligning more closely with
self-determination theory and nicely encapsulated by one student's
response,

\emph{The focus of the learning agreement should be on the student's
goals and needs, rather than on the instructor's expectations. Also,
making it more accountable, where the student should be responsible for
meeting the goals that they have set for themselves, and the instructor
should be responsible for providing the support that the student needs
to achieve those goals.}

\textbf{4.1. Limitations}

Findings of this study are inherently bound to a specific context and
time frame, influencing the generalizability of results. First and
foremost, the study's insights are derived from a first-year university
course, thereby reflecting the attitudes and experiences of students at
a particular stage in their academic journey. Students' perceptions and
acceptance of GenAI, as well as their engagement with the learning
agreement, are likely influenced by their previous experiences,
including their exposure to similar technologies during their high
school education. Attitudes toward GenAI use in educational settings are
dynamic and likely to evolve over time. The period during which this
study was conducted saw students transitioning from an environment where
GenAI tools might have been banned to one where such technologies are
increasingly embraced, not only in higher education but potentially in
future high school settings as well. This evolution suggests that the
study's findings, while insightful for the current cohort, may not fully
encapsulate the changing landscapes of GenAI acceptance and use among
incoming students. Additionally, the reliance on a single course for
data collection limits the ability to draw broader conclusions about the
effectiveness and reception of the learning agreement across diverse
academic disciplines and educational contexts.

\textbf{4.2. Future work}

Beyond ChatGPT, other GenAI tools and models such as Midjourney, Stable
Diffusion and DALL-E 2 that automatically generate videos, and images
within minutes; Eleven Labs that transforms text into audio; and Magic
Write that assist creative writing (Peres et al., 2023), can potentially
influence students' everyday use of AI to assist their coursework. The
full potential of GenAI in general and specifically on education is
still unknown and underpins a need to investigate its impact in a manner
that considers students' points of view (Ga\selectlanguage{polish}š\selectlanguage{english}evi\selectlanguage{polish}ć \selectlanguage{english}et al., 2023) and their
development. Accordingly, efforts to better align and assess the
effectiveness of the learning agreement from a self-determination theory
perspective should be explored. For example, the UNESCO guide providing
an overview of how ChatGPT works and can be used in higher education,
highlights the importance of autonomy in suggesting that guidance on
``how and when ChatGPT can be used (and when it cannot) \ldots{} should
be negotiated with students and teachers, not imposed on them''
(Sabzalieva \& Valentini, 2023; p. 13). Investigating effects on student
feelings of autonomy in comparison to other ways of communicating
academic integrity and course policies can be explored. The same UNESCO
guide suggests that GenAI should relate to course learning outcomes to
help students understand how it ``can support their learning and what
expectations there are for them'' (Sabzalieva \& Valentini, 2023; p.
13). Thereby, helping students develop a competence around figuring out
how it can best serve their needs and around what uses align with their
value systems. Future work can investigate the effectiveness of
different learning agreement designs and implementations in terms of
impact on student decision making -- and pair the use of learning
agreements with approaches to help students make more thoughtful
decisions (Arvai et al., 2004) related to GenAI use. This support of
thoughtful decision making can also explore and assess how effective
learning agreements are as tools for raising awareness of ethical
concerns as identified by researchers, policy makers, and the public.
Concerns such as discrimination -- and the need for transparency and
accountability (Nguyen et al., 2023; Akgun \& Greenhow, 2022; Miao \&
Holmes, 2021); an increasing digital divide -- accessibility limits due
to internet availability and/or regional access restrictions (Sabzalieva
\& Valentini, 2023; Miao \& Holmes, 2021); concentration of power and
abuse of power (Sabzalieva \& Valentini, 2023); surveillance and
intrusiveness (Miao \& Holmes, 2021; Akgun \& Greenhow, 2022); loss of
autonomy (Nguyen et al., 2023; Akgun \& Greenhow, 2022); obstructing
holistic competencies (Chan \& Hu, 2023); and lack of ecological
sustainability (Nguyen et al., 2023). In relation to the last aspect of
self-determination theory, relatedness, future work can compare learning
agreement construction and use at individual and group levels to
investigate the impact of relatedness on student decision making and
understanding of the ethical concerns related to GenAI use. Finally, the
design and implementation of the learning agreement can be improved
following the suggestions made by students which align with research
related to improving the effectiveness of another type of agreement,
informed consent forms. Improving readability, including causal
explanations, and supporting understanding with enhanced discussions can
help overcome cases in which student prior knowledge of the items in the
learning agreement are impoverished (Beardsley et al., 2019).

\section*{5. Conclusion}

{\label{conclusion}}

The motivation of this work was to explore whether a learning agreement
can be an acceptable tool for governing student use of GenAI in
university coursework. Results of the pilot study, with students rating
the learning agreement approach favourably at the end of the course,
suggest that despite the flaws in how it was implemented, a learning
agreement has potential to serve this role. Furthermore, the design of
the learning agreement and course modifications presented offer a
flexible path through which students can be brought to engage with
ethical discussions surrounding the use of GenAI in a situated and
relevant manner. Such agreements can go beyond university guidelines as
they can be used to both scaffold educator adaptations of guidelines to
match course needs and the rapidly changing technological landscape and
communicate the adaptations to students in an actionable manner. All in
all, learning agreements have the potential to offer an interface
through which student decision-making can be supported and interactions
among students, educators, researchers, and policy makers related to the
ethical and societal challenges of GenAI can take place.

\section*{Acknowledgements}

This work has been partially funded by UPF's PlaCLIK (E2023015714),
Erasmus+ (KA220-SCH-92001A07), the Government of Catalonia (SGR 00930),
Spanish Ministry (PID2020- 112584RB-C33 granted by
MICIN/AEI/10.13039/501100011033). DHL (Serra H\selectlanguage{ngerman}únter) acknowledges the
support by ICREA (Academia).

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\section*{Appendix}

{\label{appendix}}

\textbf{Learning Agreement for GenAI - v1 (1st Year Engineering Degree
Course)}

Introduction:

Innovations affect how we learn, work, and live. Recent innovations such
as generative artificial intelligence (AI) may lead to more rapid
changes than we have previously experienced (e.g., ChatGPT, Bard, DALL-E
-- see a recent list of tools). In this course, we hope to support your
lifelong learning. We want to help you gain experience in using
innovative technologies (that can benefit your future career) in a
responsible manner -- and for you to help us improve our definition of
what responsible use means. WHY? Despite the promises of recent
innovations such as AI tools, there are a number of concerns.~ (1)
Learners may use AI to do their assignments without learning the
material. This does not support lifelong learning. (2) Learners may use
AI in a manner that worsens societal problems such as inequality,
distrust, polarization.~ (3) Learners may use AI in a manner that
unintentionally violates (University) rules for academic integrity and
risk suspension/expulsion from the university.~ WHAT? This form is a
learning agreement. Its aim is to clarify the course rules and
expectations surrounding the responsible use of AI tools so that all
students and instructors share a common understanding.

Instructions

Read through this form carefully and submit an initial version prior to
class (B1-3). In class, ask questions to clarify any points of
confusion. Your instructor will lead a discussion on the form. At the
end of class, submit a final version (you can edit your initial
submission to make a final version of your learning agreement).

Agreement terms

Students are encouraged to use AI tools to contribute to their learning
in the course (i.e., as learning aids or to help produce designated
assignments). However, students using such tools are only permitted to
do so in accordance with the terms outlined in this form. A failure to
do so may result in a violation of university rules for academic
integrity and risk suspension/expulsion from the university. In the
(Course Name) course, I am only permitted to use innovative technologies
to assist with my course work if I agree to all 5 terms in this form:
(1) Not for assessments, (2) Declared and described usage, (3)
Appropriate citations, (4) Responsible usage, (5) Informed usage.~ For
each item, mark your selection:

{[} {]} I understand that in this (Course Name) course AI tools cannot
be used during assessment and opinion survey tasks unless specified
otherwise by your instructor.

{[} {]} I would like to discuss this further in class.

\textbf{Item \#1: Not for Assessments}

Assessments help us understand whether students have learned course
material. Opinion surveys help us understand your views on topics. The
use of AI for assessment and opinion survey tasks can defeat the purpose
of such tasks.~ However, AI tools may be useful when gathering
information to support your studying for these assessments (prior to
starting the assessment tasks). If you are not sure whether AI tools can
be used for a specific assignment, please ask your instructor.

\selectlanguage{english}\emph{- In the (Course Name) course, I am not to use AI tools when
taking assessments (e.g., quiz, test-taking) or completing opinion
surveys. The use of AI tools in these circumstances will be treated as
if I am getting help or copying another person which are violations of
(University's)} \emph{academic integrity policy.}

\textbf{Item \#2: Declared and described usage}

AI tools can be used to assist in the completion of key course
assignments (article, debate, presentation) and other assignments
explicitly designated by your instructors. However, any AI tool usage
must be declared and described in an appendix submitted with your
assignments.

\selectlanguage{english}\emph{- In the (Course Name) course, whenever an AI tool is used, the
use of the tool must be declared and described in an appendix submitted
with the course assignment. The appendix must (1) document what tool(s)
were used, (2) how the tools were used (i.e., prompts used), and (3) how
the results from the AI tools were incorporated into the submitted
work.}

\textbf{Item \#3: Appropriate citations and references}

Any content (text, image, data) from an AI tool must be cited according
to APA standards (https://libguides.mcmaster.ca/cite-gen-ai/apa). This
applies to in-text citations and references (i.e. paraphrasing, quoting,
or incorporating any content from an AI tool into your own work).~ For
example, if you copy and paste AI-generated text directly to your
assignment, you must add quotations around the text, ''the influence of
beavers on their environment can be both constructive and destructive,
contingent on the particular context'' (OpenAI, 2023). APA citation
format: Author/Developer. (Year). Model name (Version) {[}Large language
model{]}. URL Example: OpenAI. (2022). ChatGPT (December 20 version)
{[}Large Language model{]}. https://chat.openai.com/\emph{In the (Course
Name) course, if I fail to appropriately cite AI tools, it will be
considered a form of plagiarism and a violation of (University's)}
\emph{academic integrity policy.}

\textbf{Item \#4: Responsible usage}

AI tools are known to have limitations and may have problematic ethical
origins and/or intentions. Biases: Biases in datasets used to train AI
can perpetuate social inequalities and injustices. Privacy: Datasets
used to train AI may violate data privacy and intellectual property
rights. AI tools may also collect, store, and use user data for
undisclosed purposes. Misinformation: AI tools are known to fabricate
facts and can be used to spread disinformation. Power and
responsibility: Popular AI tools are closed (not transparent),
proprietary models that consume massive amounts of resources (natural,
financial) and have the power to shape public opinion.

\emph{In the (Course Name) course, I am obliged to use AI tools in a
responsible manner. This means that as part of the appendix I submit
with course assignments, I must explicitly comment on actions taken to
mitigate and/or acknowledge the known limitations of the AI tools.}

\textbf{Item \#5: Informed usage}

Usage of AI tools involves the sharing of personal data and, possibly,
intellectual property with third parties. Confidential information
should never be inputted into an AI tool as ``all content entered may
become part of the tool's dataset and may inadvertently resurface in
response to other prompts''.~

\emph{In the (Course Name) course, I have the option to use AI tools,
but I do not have to use them if I am not comfortable with sharing data
with third parties.~}

Final page

AI innovations are evolving rapidly. It is our aim to co-create (with
you) guidelines for how to best integrate AI into university coursework.
Please share any additional thoughts you have about this agreement
below.

\selectlanguage{english}
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