“We Need to Analyze Students GenAI Use”:

Towards an AI Adoption Framework for Higher Education

Lasse Bischof

1 a

, Eva-Maria Sch

2 b

, Maria Rauschenberger

2 c

and Michael Neumann

1 d

University of Applied Sciences and Arts Hannover, Hannover, Germany

University of Applied Sciences Emden/Leer, Emden, Germany

Keywords:

Generative AI, GenAI, Usage, Higher Education, Framework, Case Study.

Abstract:

Context: Generative AI (GenAI) tools such as ChatGPT are rapidly transforming how students learn and

work. While adoption among learners is high, institutional frameworks in higher education often lag behind.

Objective: This study pursues two primary objectives: 1) identifying students’ use-cases for GenAI, and 2)

synthesizing these into a systematic description how to integrate GenAI into higher education. Method: To

address these objectives, we conducted a case study at the University of Applied Sciences and Arts Hannover.

We used a questionnaire that included both quantitative and qualitative questions. Results: Our ﬁndings reveal

that 129 (n=151) of the students use GenAI tools in their studies. Based on a synthesis of the results, we

created a systematic description for GenAI integration into higher education. Contributions: We offer speciﬁc

solutions: with the AI Adoption Framework, higher education institutions will be able to review and adapt

their regulations and curricula in relation to GenAI to keep up with the pace of change in the ﬁeld.

1 INTRODUCTION

The increasing availability and advancement of gener-

ative artiﬁcial intelligence (GenAI) have led to signif-

icant transformations across various domains, includ-

ing higher education (Neumann et al., 2023). Among

the different branches of AI, GenAI tools have gained

interest due to their ability to generate human-like

text, images, code, and other forms of content (Freise

et al., 2025; Jimenez et al., 2024; R

udian et al.,

2025; Shailendra et al., 2024). These tools, such

as ChatGPT by OpenAI, have introduced new op-

portunities and challenges in higher education and

are available by the general public (Grashoff et al.,

2024; Rasheed et al., 2025; Sami et al., 2025; Zhang

et al., 2024), particularly in disciplines that involve

substantial amounts of written assignments, research,

and problem-solving (Speth et al., 2024). In the con-

text of higher education, students from diverse disci-

plines are increasingly integrating GenAI tools into

their workﬂows (von Garrel et al., 2023). The rapid

https://orcid.org/0009-0002-6622-00770

https://orcid.org/0000-0002-0410-9308

https://orcid.org/0000-0001-5722-576X

https://orcid.org/0000-0002-4220-9641

adoption of these tools has sparked discussions re-

garding their potential beneﬁts and risks in academia.

While some educators perceive them as valuable aid

in improving learning efﬁciency and fostering creativ-

ity, others raise concerns about ethical implications,

the authenticity of academic work, and potential mis-

use of plagiarism or academic dishonesty (Chen et al.,

2020).

Nowadays, higher education is challenged by sev-

eral disruptive events of the last decade (Sch

on et al.,

2023). In addition to the challenges due to the GenAI

era outlined above, the Covid-19 pandemic is a good

example. The widespread shift to online and/or dis-

tance learning during the Covid-19 pandemic has

led to a major digital transformation, as universities

around the world have adopted various tools for re-

mote working and teaching, used virtual tutoring sys-

tems, or moved to cloud repositories that provide lec-

ture materials over distance (Matthies et al., 2022;

Neumann et al., 2022).

Despite the pandemic, we see today new and

upcoming challenges at an accelerating pace as

we move towards an AI-driven era (F

orster et al.,

2024; Sch

on et al., 2023). Students with a stronger

computer science focus like information science or

e-government are leveraging these technologies for

Bischof, L., Schön, E.-M., Rauschenberger, M. and Neumann, M.

“We Need to Analyze Students GenAI Use”: Towards an AI Adoption Framework for Higher Education.

DOI: 10.5220/0013819000003985

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 21st International Conference on Web Information Systems and Technologies (WEBIST 2025), pages 429-438

ISBN: 978-989-758-772-6; ISSN: 2184-3252

429

tasks such as requirements engineering (Brocken-

brough and Salinas, 2024), code generation (Maher

et al., 2023; Savelka et al., 2023; Speth et al.,

2023), report writing (Datta, 2024), and summarizing

complex concepts (Speth et al., 2024). However,

despite the growing use of GenAI tools, the extent

to which students rely on these tools, as well as

the nature of their speciﬁc applications, remains

under-explored (Zastudil et al., 2023). Additionally,

institutional policies on AI usage in academia are still

evolving, leading to uncertainties among students

regarding acceptable AI-assisted practices in their

studies (Nithithanatchinnapat et al., 2024).

Given the profound potential of GenAI in higher

education, it is crucial to examine how students utilize

these tools (Neumann et al., 2023), identify the per-

ceived beneﬁts and challenges, and understand their

implications for academic integrity as well as learn-

ing outcomes (Chan et al., 2023).

In this paper, we aim to bridge the existing re-

search gap by examining the adoption and impact of

GenAI tools among students. Furthermore, we see a

lack in developing the curricula of the study programs

for the needed skills in the future. Thus, the above

motivates the following research questions:

• RQ 1: How do students utilize GenAI tools in

their studies?

With RQ 1, we gain an in-depth understanding

of how students use GenAI tools in their stud-

ies. We focus strongly on the computer science-

related ﬁelds of business information systems and

E-Government in one German instituion to mini-

mize biases of different academic ﬁelds and their

particularities (e.g., skill-sets).

• RQ 2: How can institutions systematically adapt

their regulations and curricula with regard to

GenAI through a structured framework?

Using insights from RQ 1, we have created a

framework to systematize our ﬁndings and pro-

vide universities with a tool to reﬁne their cur-

ricula and adapt degree program regulations for

GenAI usage.

Based on the ﬁndings of our case study, we present

the main contribution of this paper: A framework to

adopt GenAI into their study program curricula and

regulations. To be precise in wording, we understand

a framework in this paper as a systematic described

theoretical approach, which is in line with existing lit-

erature (Lederman and Lederman, 2015).

The remainder of this paper is structured as fol-

lows: Section 2 reviews related work on GenAI in

education, focusing on existing studies that have ex-

plored its adoption, beneﬁts, and challenges. Sec-

tion 3 outlines the research design, detailing the

methodology, data collection process, and analytical

approach. Section 4 presents the key ﬁndings derived

from the survey, followed by Section 5, which dis-

cusses the implications of the results for students, ed-

ucators, and institutional policies. Section 6 addresses

the study’s limitations, and Section 7 concludes the

paper with a summary of ﬁndings and directions for

future research.

2 RELATED WORK

We searched for primary studies dealing with GenAI

adoption in the Higher Educational Context. First, we

focused on existing frameworks for GenAI adoption.

(Shailendra et al., 2024) propose a 4E framework

(Embrace, Enable, Experiment, Exploit) to system-

atically integrate GenAI into higher education. The

iterative designed framework addresses curriculum

design, roles of stakeholders, training requirements,

ethical considerations, and evaluation mechanisms.

Challenges such as academic integrity, privacy, and

assessment fairness are also recognized. However, the

framework has its limitations, especially in terms of

detailed methodological guidance and empirical vali-

dation. Furthermore, there is a lack of explicit strate-

gies for addressing potential technological biases or

inaccuracies (e.g., data hallucination).

Additionally, (Su and Yang, 2023) introduced the

IDEE framework that consists of four steps. They in-

clude, e.g., outcomes identiﬁcation, as well as an ap-

plication example from practice. Though the frame-

work is introduced on a level of detail, the practical

application lacks on speciﬁc measures and actions.

(Southworth et al., 2023) present the AI iteracy

model aiming to provide an opportunity for a system-

atic curriculum development of AI courses for under-

graduate study programs. The framework consists of

ﬁve steps and is designed as an iterative approach.

Even if this frameworks has its strengths in curricu-

lum development, several other important facets such

as the focus on regulations are lacking.

To sump up, all of the identiﬁed models are

multi-step frameworks, mostly applying an iterative

approach aiming to provide a solution for the ongoing

development of new or updated GenAI tools and

technologies. However, all of the existing models

are lacking on speciﬁc aspects such as artifacts and

thus, do not provide an opportunity for us to apply

them at our universities. Besides the strong focus on

AI courses ((Southworth et al., 2023)) the identiﬁed

frameworks are mostly to detailed and heavyweight

for an application on speciﬁc study programs. The

main limitation of these frameworks is their failure to

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430

consider the speciﬁc circumstances at higher educa-

tion institution. Speciﬁcally, they often overlook how

students are currently using GenAI tools and the exist-

ing state of curricula and regulations. Consequently,

we opted to develop our own data-driven framework.

In addition to the frameworks presented in the lit-

erature, we also found studies focusing on students’

use of GenAI tools. Several empirical studies have

examined how students engage with AI-driven tools,

their frequency of use, and associated beneﬁts and

challenges. Research from University of Applied

Sciences Darmstadt (von Garrel et al., 2023) inves-

tigated the frequency and application of generative

AI tools among university students across disciplines.

The ﬁndings suggested that GenAI tools were pre-

dominantly used for text generation, summarization,

and code development. The nationwide applied sur-

vey was conducted in Germany in 2023 and focused

on student engagement with ChatGPT and similar

GenAI tools. The study revealed that approximately

63.4% of the surveyed students utilized generative AI

tools for academic purposes, with signiﬁcant varia-

tions depending on the ﬁeld of study. Another study

((Gottschling et al., 2024)) explored the implications

of AI usage in student learning environments, high-

lighting the ethical concerns and academic integrity

issues associated with AI-generated content.

Existing surveys aimed at thoroughly investigat-

ing and understanding students’ application and use

of GenAI tools are limited, particularly as they mostly

include data from disciplines other than computer sci-

ence. Therefore, we decided to design and conduct

our own survey tailored speciﬁcally to this context. A

detailed explanation of the applied research design is

provided in the next section. Moreover, these studies

have identiﬁed varying degrees of student awareness

regarding institutional AI regulations. Some univer-

sities have established clear policies, whereas others

are still in the process of deﬁning guidelines. This dis-

crepancy inﬂuences how students perceive and utilize

AI tools in their coursework, underscoring the need

for more structured institutional frameworks.

3 RESEARCH DESIGN

Our research questions focus on two main areas:

First, the speciﬁc GenAI tools students use, their ap-

plication areas, and how institutional guidelines in-

ﬂuence their adoption. Second, we aim to develop

a framework to facilitate adoption of GenAI within

higher educational institutions. To address these re-

search questions, we designed a case study according

to the guideline by ((Yin, 2009)) at the University of

Applied Sciences and Arts Hannover focusing on the

speciﬁc discipline of information systems. Figure 1

depicts the research design including the applied re-

search methods.

3.1 Case Context

The University of Applied Sciences and Arts Han-

nover is located in Germany offering education in a

wide span of disciplines such as design, engineering,

economics, and computer science. In total, around

10,000 students are enrolled in the different programs

in ﬁve faculties which are located over the city of

Hannover. Our case study focus on the information

systems discipline. The Department of Business In-

formation Systems (Faculty IV) organizes and offers

three different study programs for both a bachelor’s

and a master’s degree. Here, we took the bachelor

programs Business Information Systems (BIS) and

E-Government (EGOV) under study. In winter term

2024-2025 there were 465 of BIS students and 115

of EGOV students enrolled. A detailed overview per

term is given in Table 1.

The department is incorporating GenAI across dif-

ferent aspects of its study programs. Shortly after

the launch of ChatGPT in November 2022, a work-

ing group in the department analyzed the opportuni-

ties and risks to adapt regulations and the impact on

several exam types. Based on these analysis results,

the university adopted speciﬁc GenAI related aspects

to their regulations, especially for the bachelor thesis

and written exams. For instance, students have to re-

spect guidelines for theses and writing exams. These

guidelines provide speciﬁc regulations related to the

format (e.g., font, size, and line spacing), the cita-

tion style(s), and detailed information regarding the

use of tools, including GenAI. By explicitly address-

ing GenAI, the guidelines aim to implicitly integrate

this technology into the study programs. The last core

adaption of the curriculum of both study programs

dates back to 2018 and thus, the curricula do not de-

ﬁne GenAI skills or further measures explicitly. How-

ever, in various courses in both study programs lectur-

ers provide GenAI integration to the students. Exam-

ples are Requirements Engineering, Research Meth-

ods & Scientiﬁc Writing, or Math.

3.2 Data Collection & Analysis

Questionnaire Design: We used an online survey

as the primary data collection instrument. The choice

of this method was driven by the need to obtain a

broad and representative sample of students from the

targeted disciplines at the case institution. The ques-

“We Need to Analyze Students GenAI Use”: Towards an AI Adoption Framework for Higher Education

431

Figure 1: Research Design.

tionnaire was divided into four main parts. The ques-

tionnaire can be found at Zenodo ((Bischof et al.,

2024)). The ﬁrst part focused on demographic infor-

mation, gathering details such as the study program.

This information allowed for the categorization of re-

sponses and ensured that only relevant data from BIS

and EGOV students were included in the analysis.

The second part explored students’ attitudes to-

ward GenAI tools. It assessed their familiarity with

AI-based applications, their perceived usefulness, and

their awareness of university policies concerning AI

use. Responses in this part were recorded using a 5-

point Likert scale, ranging from ”strongly disagree”

to ”strongly agree”, allowing for a nuanced under-

standing of student perspectives.

The third part investigated usage patterns and pol-

icy compliance, asking students whether they used

GenAI tools and, if so, which ones. It also in-

quired about their primary use cases, such as assist-

ing with research, programming, summarizing aca-

demic texts, or preparing for exams. Additionally,

this part assessed whether students believed their AI

usage aligned with university guidelines, which pro-

vided insight into potential gaps in institutional com-

munication regarding AI regulations.

The ﬁnal part covered perceived beneﬁts and chal-

lenges associated with the use of GenAI in academic

settings. While most of the questionnaire employed

structured, multiple-choice questions, this part also

included open-ended questions. This allowed stu-

dents to elaborate on their experiences, including

speciﬁc advantages and difﬁculties they encountered

while integrating AI tools into their learning process.

Data Collection and Sampling Strategy: Con-

ducted between November 5, 2024, and December

31, 2024, the survey was hosted on LimeSurvey. Par-

ticipation was entirely voluntary and anonymous to

mitigate social desirability bias and ensure compli-

ance with ethical standards related to student privacy.

To enhance the reliability of the data, the survey

underwent a pilot phase involving 20 students from

BIS and EGOV. Their feedback contributed to reﬁn-

ing the questionnaire by improving clarity, ease of un-

derstanding, and the structure of the response format.

Adjustments made after the pilot phase included re-

wording complex questions and modifying the layout

for better readability.

The survey was distributed through multiple com-

munication channels to maximize participation. Lec-

turers promoted the study in relevant courses, and QR

codes linking to the survey were displayed in class-

rooms. Additionally, students received direct invita-

tions via email and online university portals, ensuring

that the target audience was adequately reached.

A non-probabilistic sampling approach was em-

ployed, meaning that students chose to participate

voluntarily rather than being selected through a ran-

domized process. While this method facilitated efﬁ-

cient data collection, it also introduced potential bias,

as students with an interest in AI might have been

more inclined to respond.

Data Analysis: The ﬁnal data set (N = 151) was

reviewed and only includes fully completed submis-

sions. The analysis was conducted using statistical

software to ensure accurate processing. The dataset

was cleaned and managed using Python’s Pandas li-

brary, while Matplotlib and Seaborn were used for

visualizing trends. Further statistical computations,

such as conﬁdence intervals and signiﬁcance testing,

were performed using SciPy to verify the reliability

of the ﬁndings.

The multiple-choice responses were analyzed

quantitatively, with frequency distributions and com-

parative analyses conducted to identify patterns in

GenAI tool adoption across different student groups.

Open-ended responses were processed using a the-

matic analysis approach (according to (Cruzes and

Dyba, 2011)), in which recurring themes related to

the beneﬁts and challenges of AI adoption were cate-

gorized and interpreted.

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432

Table 1: Number of students by term and study program.

Term BIS EGOV Total Count

1 33 13 46

2 16 2 18

3 19 20 39

4 5 0 5

5 7 17 24

6 6 0 6

7 4 6 10

3 0 3

Overall Total 93 58 151

Sample Description: A total of 151 students par-

ticipated in the survey, all of whom were enrolled

at the case university. The sample consisted of

93 BIS (61.59%) and 58 EGOV students (38.41%).

The sample distribution reﬂects the general student

composition of these programs, although students in

term 5 were underrepresented due to external intern-

ships during the survey period. Table 1 provides an

overview of the students enrolled per term and study

program.

In terms of academic level, most participants were

in their ﬁrst to fourth term, while students from the

ﬁfth and seventh terms were signiﬁcantly less repre-

sented. This is attributed to their participation in off-

campus practical training phases, which limited their

engagement with university-related activities. How-

ever, keeping the total count of enrolled students in

both programs in mind, we have a statistical signiﬁ-

cant sample for both program (for BIS: 93 out of 465

or 20%; for EGOV: 58 out of 115 or around 50%).

Ethical Considerations: To maintain ethical in-

tegrity, strict measures were implemented throughout

the research process. Participation was fully volun-

tary, and students had the opportunity to withdraw at

any stage without justifying. The survey was entirely

anonymous, ensuring that no personal identiﬁers were

collected. Additionally, formal approval from the uni-

versity was obtained before data collection, ensuring

that the study adhered to ethical research guidelines.

Synthesis and Framework Creation: We ana-

lyzed questionnaire data alongside institutional doc-

uments to examine the use of GenAI tools, focusing

particularly on students’ use-cases and existing regu-

lations governing their permitted usage.

Based on a thorough analysis of our datasets, we

ﬁrst synthesized our ﬁndings. We then discussed

which recommendations for GenAI adoption could be

identiﬁed. In a second step, considering the relation-

ships among these recommendations, we developed a

framework and elevated our approach for the selected

case to a meta-level, aiming to provide a solution ap-

plicable to other academic institutions.

4 RESULTS & DISCUSSION

Here, we answer our ﬁrst research questions aiming to

provide in-depth ﬁndings as a basis for the next sec-

tion in which we will present our AI adoption frame-

work for higher educational contexts.

4.1 GenAI Adoption by Students

First, we present the answer of RQ 1: How do stu-

dents utilize GenAI tools in their studies?

One of the key ﬁndings of this study is the high

adoption rate of GenAI tools among students. Of

the 151 students surveyed, 129 students (85.43%) re-

ported that they actively use GenAI tools in their stud-

ies. Conversely, 19 students (12.58%) indicated that

they do not use these tools, while 3 students (1.99%)

abstained from answering. A breakdown of usage by

study program reveals that 88.17% of BIS students

and 81.03% of EGOV students use GenAI. This sug-

gests that AI adoption is slightly higher among BIS

students, possibly due to the stronger emphasis on

computer science discipline and speciﬁc courses for

programming and data-driven applications.

GenAI tools used by students: Among the stu-

dents who use GenAI, ChatGPT is by far the most

frequently used tool, with 93.8% of whom use it reg-

ularly. Other tools, such as Google Bard and DeepL

Write, were mentioned but to a much lesser extent.

The widespread preference for ChatGPT may be due

to its intuitive user interface, availability, and broad

range of functionalities. Additionally, students in

computer science-related ﬁelds may favor ChatGPT

over other AI tools due to its strong capabilities in

code generation and debugging.

When asked about paid AI subscriptions, the vast

majority of the AI-using respondents (85.27%) re-

ported that they rely exclusively on free versions of

AI tools. Only 19 students (14.73%) indicated that

they use paid AI services, citing greater functional-

ity and improved response accuracy as their primary

reasons.

Use Cases of GenAI in Academic Work: Students

use GenAI tools for a variety of academic tasks, with

the most common applications being:

“We Need to Analyze Students GenAI Use”: Towards an AI Adoption Framework for Higher Education

433

• Research Assistance (40.15%): AI is frequently

used to summarize academic papers, generate ex-

planations, and structure research topics.

• Programming Support (37.8%): A large percent-

age of students, particularly from BIS, use GenAI

for coding help, debugging, and generating sam-

ple code snippets.

• Text Summarization (34.65%): Many students

rely on GenAI tools to condense lecture notes,

academic papers, and textbooks into more di-

gestible formats.

Other reported applications include exam prepa-

ration, language translation, and writing assistance

for essays and reports (also known as written ex-

ams). However, students also acknowledged that

over-reliance on GenAI for academic tasks can limit

critical thinking skills and independent problem-

solving.

Perceived challenges of GenAI usage by students:

A major concern revealed in the survey is the lack

of clarity surrounding institutional regulations on AI

usage.

The majority of students, regardless of most aca-

demic terms (with the exception of term 6), report

that existing regulations are unknown to them. We

initially expected that the number of students familiar

with the regulations would steadily increase with each

semester, while the number of those unaware of them

would decrease accordingly. However, the data sug-

gest that a signiﬁcant degree of uncertainty regarding

existing rules and regulations persists until the end of

the study program. Consequently, we investigated the

extent to which students believe their use of GenAI

adheres to established guidelines and regulations:

• 43.7% of students were unsure whether their AI

usage was compliant with academic policies.

• 37.2% believed their use was aligned with the uni-

versity’s rules.

• 19.1% admitted they might be violating institu-

tional policies.

This high degree of uncertainty highlights a gap

in institutional communication, as many students re-

ported a lack of clear AI usage policies from faculty

members and university administration. Nevertheless,

based on our document screening, we found that the

internal university communication rules imply men-

tioning existing guidelines and regulations. Thus,

we also see a lack of speciﬁc skills (or motivation)

on the student side to verify the existing guidelines

for GenAI usage rules. However, one may assume

that some students may unknowingly violate aca-

demic integrity standards due to unclear or inconsis-

tent enforcement. Furthermore, concerns about over-

reliance on GenAI tools were also raised. A signiﬁ-

cant portion of students warned that relying too heav-

ily on AI can impede their ability to develop a deeper

understanding, diminish critical thinking skills, and

promote passive learning habits. Furthermore, 12 stu-

dents explicitly mentioned concerns related to data

privacy, particularly regarding the storage and use of

personal input data by AI companies.

In conclusion of our ﬁndings on the use of GenAI

by students, we compared our ﬁndings to the re-

sults from the existing literature. Here, we focused

our comparison to a previous nationwide survey((von

Garrel et al., 2023)), which reported that only 63.4%

of students in Germany used GenAI. In contrast, our

study found a signiﬁcantly higher GenAI usage rate

of 85.43%. A Chi-square test conﬁrmed that this

difference is statistically signiﬁcant (χ

= 30.09, p ¡

4.1 × 10

−5

). However, the survey ((von Garrel et al.,

2023)) was conducted in 2023 and considered a wide

spread of study programs, while ours focused strongly

on information systems and related disciplines. Thus,

the results discrepancy may be due to the increasing

prevalence of GenAI tools over time, or the higher

afﬁnity of business informatics and e-government stu-

dents for GenAI technologies compared to students

from other disciplines. Nevertheless, especially uni-

versities providing study programs with a relation

to computer science, information systems or similar

should be aware that GenAI adoption by the mass of

their students may be the reality; for our case univer-

sity it is. Thus, universities should aim to integrate AI

literacy into academic programs, ensure clear policy

communication, and encourage responsible AI use.

By doing so, they can prepare students for an increas-

ingly AI-driven world while preserving the integrity

of higher education.

5 GenAI ADOPTION

FRAMEWORK FOR HIGHER

EDUCATION

The ﬁndings of this study underscore the transforma-

tive impact of GenAI on higher education. As GenAI

tools become increasingly sophisticated, educational

institutions must strike a balance between leveraging

AI’s beneﬁts and maintaining academic integrity. Our

case study results suggest that while students readily

adopt GenAI, institutional policies have not yet fully

adapted to this technological shift. Moving forward,

WEBIST 2025 - 21st International Conference on Web Information Systems and Technologies

434

a proactive and informed approach is essential. Based

on a thorough analysis of our ﬁndings, we answer our

second research question: How can institutions sys-

tematically adapt their regulations and curricula with

regard to GenAI through a structured framework?

Our AI Adoption Framework enables higher ed-

ucation institutions to review and update their regu-

lations and curricula in response to GenAI advance-

ments, ensuring they keep pace with rapid changes

in the ﬁeld. The framework consists of four iterative

steps, detailed in the following and illustrated in Fig-

ure 2.

I. Document Analysis. The ﬁrst step comprises a

document analysis. When analyzing documents, all

internal documents such as examination regulations

or guidelines, and curricula, including module de-

scriptions, should be taken into account. It is also

useful to consider external documents on the use and

functionality of AI tools and models, or general poli-

cies such as the EU AI Act, or scientiﬁc studies fo-

cusing on the discipline should also be included.

II. Survey with quantitative and qualitative items.

In the second step, a questionnaire will be developed

based on the results of the document analysis. It is

a challenge to conduct the survey with a representa-

tive number of students. Therefore, we recommend

combining the survey with other data sources to ob-

tain comprehensive insights into GenAI usage. Ad-

ditionally, study programs should be analyzed sepa-

rately to minimize disciplinary biases, allowing the

questionnaire to reﬂect the speciﬁcs of individual pro-

grams or subject areas. Furthermore, regular reviews

and iterative adjustments of the questionnaire are ad-

visable, especially to account for emerging develop-

ments, such as new GenAI models or policy changes.

III. Synthesize ﬁndings. Based on the ﬁndings of

the previous two steps, it is now necessary to synthe-

size the ﬁndings in order to make speciﬁc recommen-

dations for the adaptation of internal institutional reg-

ulations and/or the curriculum.

IV. Update regulations and curricula. The recom-

mendations developed in this step serve as an au-

thoritative and well-founded basis for the actual up-

dating of internal institutional regulations, guidelines,

or even curricula. This usually requires cooperation

in committees and organizational units of an aca-

demic institution and might be correspondingly time-

consuming. This underscores the importance of thor-

oughly preparing for the update.

After the ﬁrst run of this described process, the it-

erative transition back to the ﬁrst step of the frame-

work takes place (shown in blue in the Figure 2).

Given the highly dynamic nature of the GenAI mar-

ket, we anticipate regular releases of new models and

enhancements in functionality. We also expect other

vendors to enter the market and present and release

optimized GenAI models. This requires a regular re-

view of internal documents and, in particular, external

documents, such as new or amended laws, guidelines,

and documentation from GenAI providers or scien-

tiﬁc ﬁndings on the use of GenAI in practice and re-

search. As a result, it can be assumed that the use of

GenAI by students will also change regularly, which

in turn will lead to recurring surveys. Especially in

academic institutions, where changes in curricula and

regulations are subject to complex processes, careful

preparation of these adjustments is necessary. Our

proposed iterative framework can efﬁciently organize

efforts and consistently integrate new ﬁndings into the

ongoing development of curricula and regulations.

6 LIMITATIONS

As detailed in Section 3, we employed a system-

atic approach to prepare and conduct our case study.

However, all studies have limitations. In this section,

we address these limitations using the validity threats

concept outlined by Runeson and Hoest (Runeson and

ost, 2009).

Construct Validity. The survey questions may have

introduced interpretation bias, particularly regarding

terms like ”academic integrity” and ”rule compli-

ance”, due to the absence of clear university guide-

lines on GenAI usage. Additionally, self-reported

GenAI usage may be inaccurate due to social desir-

ability bias, despite anonymity. The framing of ques-

tions, including speciﬁc GenAI tool examples, may

have led to an anchoring effect, inﬂuencing respon-

dents’ selections rather than capturing their broader

GenAI usage patterns.

Internal Validity. As this study relies on cross-

sectional survey data, it identiﬁes associations rather

than causal effects. A key confounding variable

is students’ prior technical expertise, as those with

programming or data science backgrounds may be

more inclined to use GenAI tools, potentially skew-

ing results. Since this factor was not explicitly con-

trolled for, GenAI adoption rates may be overesti-

mated. Additionally, the timing of the survey (win-

ter term 2024/2025) may have inﬂuenced participa-

“We Need to Analyze Students GenAI Use”: Towards an AI Adoption Framework for Higher Education

435

Figure 2: Visualization of the GenAI Adoption Framework.

tion, with later-semester students, particularly those

in internships, being underrepresented, leading to se-

lection bias. Furthermore, the study did not distin-

guish between voluntary GenAI adoption and manda-

tory use, which is essential for understanding stu-

dents’ true motivations but was not accounted for.

External Validity. The study’s restriction to the

case context limits its generalizability, as GenAI poli-

cies and educational cultures may differ across insti-

tutions. Language barriers may have led to the under-

representation of international students, reducing the

diversity of perspectives. Additionally, disparities in

digital literacy and ﬁnancial resources were not con-

sidered, meaning students with limited access to pre-

mium GenAI tools may engage with them differently,

potentially skewing the ﬁndings.

Conclusion Validity. The survey study relied on

descriptive statistics from Likert-scale responses, lim-

iting the ability to establish statistical signiﬁcance or

detect subtle GenAI usage patterns. Open-ended re-

sponses were analyzed qualitatively, but subjective

categorization may have introduced bias, potentially

overlooking nuances. Additionally, survey dropout

rates could have skewed results, as students more en-

gaged with GenAI or willing to complete surveys may

be overrepresented. Without data on dropout reasons,

the impact on ﬁndings remains unclear.

7 CONCLUSION AND FUTURE

WORK

Our study investigated the adoption of GenAI tools

among students in information systems disciplines at

a German university of applied sciences, addressing

how students utilize these tools and how institutions

can systematically integrate GenAI into their curric-

ula and regulations.

The key contribution of our paper is the GenAI

Adoption Framework for Higher Education. To facil-

itate structured GenAI integration, we developed an

iterative approach consisting of four steps: 1) Docu-

ment Analysis, 2) Quantitative and Qualitative Sur-

veys, 3) Synthesis of Findings, and 4) Updating regu-

lations and curricula. Our framework assists institu-

tions in proactively adapting, promoting responsible

AI usage, and preserving academic integrity as tech-

nology rapidly evolves.

Furthermore, our ﬁndings indicate that a signiﬁ-

cant majority of students actively engage with GenAI

tools in their academic work: 85.43% of surveyed

students use AI-powered applications, with ChatGPT

being the most frequently mentioned tool. The pri-

mary use cases include research (40.15%), program-

ming (37.8%), and explanations or learning support

(34.65%). However, despite widespread adoption,

there remains substantial uncertainty regarding insti-

tutional policies governing the use of GenAI. Over

71% of students were unsure whether their AI us-

age aligns with university guidelines. This suggests

a communication gap between regulatory bodies and

students.

Our future research will go in two directions.

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First, we want to further evaluate our AI adoption

framework. To do this, we want to conduct more

case studies at other institutions and gain more experi-

ence with the framework in order to improve it. Sec-

ondly, we want to iteratively improve the guidelines

and curricula at our own university to keep pace with

the rapid developments in the ﬁeld of GenAI. Further-

more, future research should examine how reliance

on GenAI impacts students’ cognitive skills, includ-

ing critical thinking, problem-solving, and creativity.

In particular, our case study suggests that many

students lack awareness of GenAI regulations, but the

reasons behind this gap are unclear. Further studies

should investigate why students are uninformed about

policies—whether this is due to ineffective communi-

cation, lack of interest, or ambiguity in guidelines. Fi-

nally, research should explore how GenAI tools might

evolve to better serve educational contexts. As GenAI

technology advances, institutions must continuously

adapt their teaching strategies and assessment meth-

ods to reﬂect the evolving capabilities of these tools.

ACKNOWLEDGEMENTS

We would like to thank the students who took part in

our survey on their GenAI usage. GenAI tools (Chat-

GPT, Deepl, and Grammarly) were used for the opti-

mization of text passages.

REFERENCES

Bischof, L., Rauschenberger, M., Sch

on, E.-M., and Neu-

mann, M. (2024). Questionnaire genai usage by stu-

dents.

Brockenbrough, A. and Salinas, D. (2024). Using genera-

tive ai to create user stories in the software engineering

classroom. In Proc. of the 36th Intnl. Conference on

Software Engineering Education and Training, pages

1–5.

Chan, M. M., Amado-Salvatierra, H. R., Hernandez-

Rizzardini, R., and De La Roca, M. (2023). The po-

tential role of ai-based chatbots in engineering educa-

tion. experiences from a teaching perspective. In Proc.

of the Frontiers in Education Conference, pages 1–5.

Chen, L., Chen, P., and Lin, Z. (2020). Artiﬁcial intelli-

gence in education: A review. IEEE Access, 8:75264–

75278.

Cruzes, D. S. and Dyba, T. (2011). Recommended steps for

thematic synthesis in software engineering. In Proc.

of the Intnl. Symposium on Empirical Software Engi-

neering and Measurement, page 275–284. IEEE.

Datta, S. (2024). Using generative artiﬁcial intelligence

tools in software engineering courses. In Proc. of the

36th Intnl. Conf. on Software Engineering Education

and Training, pages 1–2.

Freise, L. R., Bruhin, O., Ritz, E., Li, M. M., and Leimeis-

ter, J. M. (2025). Code and craft: How generative ai

tools facilitate job crafting in software development.

In Proc. of the 58th Hawaii Intnl. Conf. on System Sci-

ences.

orster, M., Pitz, K., Wrabel, A., Klier, M., and Zimmer-

mann, S. (2024). Building ai literacy with experiential

learning – insights from a ﬁeld experiment in k-12 ed-

ucation. In Wirtschaftsinformatik 2024 Proceedings.

Gottschling, S., Seidl, T., and Vonhof, C. (2024). Nutzung

von KI-tools durch studierende. (11).

Grashoff, I., Mayer, T., and Recker, J. (2024). Challenges,

success factors, and potential beneﬁts of genai devel-

opment: Insights from an ongoing case study at a ger-

man automotive manufacturer. In ICIS 2024 Proceed-

ings.

Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press,

O., and Narasimhan, K. R. (2024). SWE-bench: Can

language models resolve real-world github issues? In

Proc. of the 12th Intnl. Conf. on Learning Representa-

tions.

Lederman, N. and Lederman, J. (2015). What is a theo-

retical framework? a practical answer. J Sci Teacher

Educ, 26:593—-597.

Maher, M. L., Tadimalla, S. Y., and Dhamani, D. (2023).

An exploratory study on the impact of ai tools on the

student experience in programming courses: an inter-

sectional analysis approach. In Proc. of the Frontiers

in Education Conference, pages 1–5.

Matthies, C., Teusner, R., and Perscheid, M. (2022). Chal-

lenges (and opportunities!) of a remote agile software

engineering project course during covid-19. In Proc.

of the 55th Hawaii Intnl. Conf. on System Sciences.

Neumann, M., M

oteﬁndt, D., Linke, L., Radtke, D.,

Mattst

adt, A., Herzig, F., and Regel, P. (2022). How

to ﬁll the gap between practice and higher education:

Performing eduscrum with real world problems in vir-

tual distance teaching. In Proc. of the 17th Intnl. Conf.

on Software Engineering Advances, pages 1–5.

Neumann, M., Rauschenberger, M., and Sch

on, E.-M.

(2023). “we need to talk about chatgpt”: The future

of ai and higher education. In Proc. of the 5th Intnl.

Workshop on Software Engineering Education for the

Next Generation, pages 29–32.

Nithithanatchinnapat, B., Maurer, J., Deng, X. N., and

Joshi, K. D. (2024). Future business workforce:

Crafting a generative ai-centric curriculum today for

tomorrow’s business education. SIGMIS Database,

55(1):6–11.

Rasheed, Z., Waseem, M., Sami, M. A., Kemell, K.-K., Ah-

mad, A., Duc, A. N., Syst

a, K., and Abrahamsson,

P. (2025). Autonomous agents in software develop-

ment: A vision paper. In Marchesi, L., Goldman,

A., Lunesu, M. I., Przybyłek, A., Aguiar, A., Morgan,

L., Wang, X., and Pinna, A., editors, Agile Processes

in Software Engineering and Extreme Programming

– Workshops, pages 15–23, Cham. Springer Nature

Switzerland.

“We Need to Analyze Students GenAI Use”: Towards an AI Adoption Framework for Higher Education

437

udian, S., Podelo, J., Ku

ılek, J., and Pinkwart, N.

(2025). Feedback on feedback: Student’s perceptions

for feedback from teachers and few-shot llms. In Pro-

ceedings of the 15th International Learning Analytics

and Knowledge Conference, LAK ’25, page 82–92,

New York, NY, USA. Association for Computing Ma-

chinery.

Runeson, P. and H

ost, M. (2009). Guidelines for conduct-

ing and reporting case study research in software engi-

neering. Empirical Software Engineering, 14(2):131–

164.

Sami, M. A., Waseem, M., Zhang, Z., Rasheed, Z., Syst

K., and Abrahamsson, P. (2025). Early results of an

ai multiagent system for requirements elicitation and

analysis. In Pfahl, D., Gonzalez Huerta, J., Kl

under,

J., and Anwar, H., editors, Product-Focused Soft-

ware Process Improvement, pages 307–316, Cham.

Springer Nature Switzerland.

Savelka, J., Agarwal, A., Bogart, C., Song, Y., and Sakr,

M. (2023). Can generative pre-trained transformers

(gpt) pass assessments in higher education program-

ming courses? In Proc. of the Conf. on Innovation

and Technology in Computer Science Education V. 1,

page 117–123, New York, NY, USA. Association for

Computing Machinery.

Sch

on, E.-M., Neumann, M., Hofmann-St

olting, C., Baeza-

Yates, R., and Rauschenberger, M. (2023). How are

ai assistants changing higher education? Frontiers in

Computer Science, 5.

Sch

on, E.-M., Buchem, I., Sostak, S., and Rauschenberger,

M. (2023). Shift toward value-based learning: Ap-

plying agile approaches in higher education. In Mar-

chiori, M., Dom

ınguez Mayo, F. J., and Filipe, J.,

editors, Web Information Systems and Technologies,

pages 24–41, Cham. Springer Nature Switzerland.

Shailendra, S., Kadel, R., and Sharma, A. (2024). Frame-

work for adoption of generative artiﬁcial intelligence

(genai) in education. IEEE Transactions on Educa-

tion, 67(5):777–785.

Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed,

D., McCarty, C., Brendemuhl, J., and Thomas, A.

(2023). Developing a model for ai across the curricu-

lum: Transforming the higher education landscape via

innovation in ai literacy. Computers and Education:

Artiﬁcial Intelligence, 4:100127.

Speth, S., Meißner, N., and Becker, S. (2023). Investigat-

ing the use of ai-generated exercises for beginner and

intermediate programming courses: A chatgpt case

study. In Proc. of the 35th Intnl. Conf. on Software

Engineering Education and Training, pages 142–146.

Speth, S., Meißner, N., and Becker, S. (2024). Chatgpt’s

aptitude in utilizing uml diagrams for software engi-

neering exercise generation. In Proc. of the 36th Intnl.

Conf. on Software Engineering Education and Train-

ing, pages 1–5.

Su, J. and Yang, W. (2023). Unlocking the power of chatgpt:

A framework for applying generative ai in education.

ECNU Rev. Educ., 6(3):355–366.

von Garrel, J., Mayer, J., and M

uhlfeld, M. (2023).

unstliche intelligenz im studium eine quantitative

befragung von studierenden zur nutzung von Chat-

GPT & co.

Yin, R. K. (2009). Case study research: Design and meth-

ods, volume 5 of Applied social research methods se-

ries. Sage, 4th edition.

Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., and Mac-

Neil, S. (2023). Generative ai in computing education:

Perspectives of students and instructors. In Proc. of

the Frontiers in Education Conference, pages 1–9.

Zhang, Z., Rayhan, M., Herda, T., Goisauf, M., and Abra-

hamsson, P. (2024). Llm-based agents for automat-

ing the enhancement of user story quality: An early

report. In Agile Processes in Software Engineering

and Extreme Programming, pages 117–126, Cham.

Springer Nature Switzerland.

WEBIST 2025 - 21st International Conference on Web Information Systems and Technologies

438