pith. sign in

arxiv: 2604.22030 · v1 · submitted 2026-04-23 · 💻 cs.CY · cs.SE

A Systematic AI Adoption Framework for Higher Education: From Student GenAI Usage to Institutional Integration

Michael Neumann , Lasse Bischof , Maria Rauschenberger , Eva-Maria Sch\"on This is my paper

Pith reviewed 2026-05-08 13:39 UTC · model grok-4.3

classification 💻 cs.CY cs.SE
keywords generative AIhigher educationAI adoption frameworkacademic integrityinstitutional adaptationstudent usagecase studypolicy gaps
0
0 comments X

The pith

An iterative framework lets universities close regulatory gaps by tracking student generative AI use and updating rules accordingly.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines how students at one German university of applied sciences employ generative AI tools such as ChatGPT for research, programming, and writing tasks. It identifies widespread adoption alongside confusion about institutional policies and gaps in existing regulations. The authors build an operational model that repeatedly combines document analysis with student surveys to inform targeted revisions of governance, assessments, and curricula. A reader would care because this approach offers a concrete way for institutions to reduce academic integrity risks and assessment inconsistencies as AI tools become routine. The framework is presented as lightweight and iterative so it can keep pace with rapid technological change.

Core claim

The central claim is that the AI Adoption Framework for Higher Education supplies an iterative and operational process that integrates document analysis, empirical observation of student practices, synthesis of findings, and targeted updates of regulations and curricula. In the reported case study this process revealed high generative AI usage for academic support but also regulatory ambiguity and student uncertainty about rules. The framework therefore addresses governance, assessment validity, and academic integrity by feeding observed usage patterns back into policy and teaching adjustments.

What carries the argument

The AI Adoption Framework for Higher Education: an iterative cycle that combines ongoing document analysis, empirical observation of student generative AI usage, synthesis of results, and targeted revisions to regulations and curricula.

If this is right

  • Regulatory documents and curricula can be revised on a regular cycle to reduce ambiguity around acceptable AI assistance.
  • Assessment methods can be adjusted to preserve validity while accounting for common student uses of generative AI.
  • Clearer policies informed by actual usage data can lower risks of academic integrity violations.
  • Curricula can incorporate guidance on appropriate AI use to align teaching with student practices.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same observation-to-update cycle could be applied to other fast-changing educational technologies beyond generative AI.
  • Institutions adopting the framework might benefit from measuring changes in student policy awareness before and after updates.
  • Cross-institutional comparisons using the framework could identify which elements are universal and which require local tailoring.

Load-bearing premise

The assumption that patterns of AI use and policy shortcomings found at one German university of applied sciences will hold for higher education institutions in other countries, sizes, and disciplines.

What would settle it

Repeating the survey and document analysis at multiple universities in different countries and disciplines and obtaining substantially different student usage rates or regulatory gaps would undermine the claim that the framework generalizes.

Figures

Figures reproduced from arXiv: 2604.22030 by Eva-Maria Sch\"on, Lasse Bischof, Maria Rauschenberger, Michael Neumann.

Figure 1
Figure 1. Figure 1: Research Design [3] 3.1 Case Context The University of Applied Sciences and Arts Hannover is located in Germany offering education in a wide span of disciplines such as design, engineering, eco￾nomics, 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 di… view at source ↗
Figure 2
Figure 2. Figure 2: Visualization of the AI Adoption Framework view at source ↗
read the original abstract

The rapid development of GenAI technologies is transforming learning, assessment, and academic production in higher education. Despite increasing student adoption, many institutions lack operational mechanisms to systematically align regulations and curricula with evolving generative artificial intelligence practices, creating regulatory ambiguity and academic integrity risks. This study investigates how students utilize generative artificial intelligence tools in computer science-oriented disciplines and develops a structured, lightweight framework supporting institutional adaptation to pervasive GenAI usage. We conducted a case study at the University of Applied Sciences and Arts Hannover (Germany), combining document analysis with an online survey (N = 151) targeting Business Information Systems and E-Government students. Quantitative responses were analyzed statistically, while open-ended responses underwent thematic synthesis. Generative artificial intelligence adoption was widespread, with ChatGPT as the dominant tool. Students primarily used generative artificial intelligence for research assistance, programming support, and text processing. However, substantial policy uncertainty was observed: many students were unaware of or unsure about institutional generative artificial intelligence regulations. Document analysis revealed regulatory gaps, ambiguous terminology, and inconsistencies between formal rules and teaching practices. To address these shortcomings, we propose the AI Adoption Framework for Higher Education, an iterative and operational model integrating document analysis, empirical observation, synthesis of findings, and targeted updates of regulations and curricula. The framework addresses governance, assessment validity, and academic integrity under generative artificial intelligence conditions and provides practical guidance for institutional adaptation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents findings from a case study at the University of Applied Sciences and Arts Hannover, Germany. Through document analysis and an online survey of 151 students in Business Information Systems and E-Government programs, it documents widespread use of generative AI tools, particularly ChatGPT, for research assistance, programming support, and text processing. The study also highlights substantial policy uncertainty among students and gaps in institutional regulations. To address these issues, the authors propose the AI Adoption Framework for Higher Education, an iterative model that combines document analysis, empirical observation, synthesis of findings, and targeted updates to regulations and curricula, focusing on governance, assessment validity, and academic integrity.

Significance. The local observations on GenAI adoption and regulatory challenges are well-supported by the survey data and document analysis, offering practical insights for similar institutions. The proposed framework represents a constructive attempt to provide operational guidance for higher education adaptation to GenAI. If the framework's components can be validated or adapted more broadly, it could contribute to the development of systematic approaches in the field. The mixed-methods design is a positive aspect of the work.

major comments (1)
  1. [Abstract] Abstract: The abstract claims that the study develops 'a structured, lightweight framework supporting institutional adaptation' and proposes 'the AI Adoption Framework for Higher Education, an iterative and operational model' for higher education. This central claim of general applicability rests on synthesis from a single-institution case study without reported validation or testing in other contexts, which is a load-bearing issue for the framework's positioning as a systematic model.
minor comments (2)
  1. [Methods] The manuscript would benefit from more detail on the statistical analysis methods used for quantitative responses and the thematic synthesis process for open-ended responses to allow for better assessment of the findings' robustness.
  2. [Discussion] Clarifying the distinction between the empirical findings specific to the Hannover case and the general framework recommendations would improve the manuscript's clarity and prevent overgeneralization.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive review and for recognizing the value of the local observations, survey data, document analysis, and mixed-methods design. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The abstract claims that the study develops 'a structured, lightweight framework supporting institutional adaptation' and proposes 'the AI Adoption Framework for Higher Education, an iterative and operational model' for higher education. This central claim of general applicability rests on synthesis from a single-institution case study without reported validation or testing in other contexts, which is a load-bearing issue for the framework's positioning as a systematic model.

    Authors: We agree that the framework is synthesized from a single-institution case study and has not been validated or tested in other settings. The manuscript presents the AI Adoption Framework as an iterative model whose components (document analysis, empirical observation, synthesis, and targeted updates) are derived directly from the Hannover findings on regulatory gaps, student uncertainty, and GenAI usage patterns. While these issues are common across higher education, we do not claim empirical validation beyond the case. To address the concern, we will revise the abstract to state explicitly that the framework is proposed on the basis of the single-institution case study and is offered as an adaptable starting point for other institutions to apply through their own iterative cycles, rather than as a pre-validated systematic model. revision: yes

Circularity Check

0 steps flagged

No circularity: framework synthesized inductively from case-study observations

full rationale

The paper conducts document analysis and a survey (N=151) at one German UAS, then synthesizes findings into the proposed AI Adoption Framework. This is a standard inductive proposal step with no equations, fitted parameters renamed as predictions, self-definitional loops, or load-bearing self-citations. The framework is presented as an output of the empirical synthesis rather than a restatement of inputs by construction. No uniqueness theorems or ansatzes are imported via citation. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that self-reported survey responses accurately capture usage and awareness, plus the premise that a single-institution case can ground a transferable framework.

axioms (1)
  • domain assumption Self-reported survey data from students accurately reflects actual generative AI usage and knowledge of institutional policies.
    The quantitative and thematic analysis depends on honest reporting without independent verification of actual tool usage.
invented entities (1)
  • AI Adoption Framework for Higher Education no independent evidence
    purpose: Iterative model to integrate document analysis, empirical data, and policy/curriculum updates for GenAI adaptation.
    Newly proposed construct derived from this case study; no external validation or falsifiable test outside the paper is described.

pith-pipeline@v0.9.0 · 5557 in / 1374 out tokens · 87079 ms · 2026-05-08T13:39:10.474003+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

39 extracted references · 39 canonical work pages

  1. [1]

    Contemporary Educational Technology16(4) (2015)

    Aldossary, A.S., Aljindi, A.A., Alamri, J.M.: The role of generative ai in educa- tion: Perceptions of saudi students. Contemporary Educational Technology16(4) (2015). https://doi.org/\url {10.30935/cedtech/15496}

    work page doi:10.30935/cedtech/15496 2015

  2. [2]

    Bischof, L., Rauschenberger, M., Schön, E.M., Neumann, M.: Questionnaire genai usage by students (2024), https://doi.org/10.5281/zenodo.15017474

    work page doi:10.5281/zenodo.15017474 2024

  3. [3]

    we need to an- alyze students genai use

    Bischof, L., Schön, E.M., Rauschenberger, M., Neumann, M.: “we need to an- alyze students genai use”: Towards an ai adoption framework for higher ed- ucation. In: Proceedings of the 21st International Conference on Web Infor- mation Systems and Technologies. pp. 429–438. INSTICC, SciTePress (2025). https://doi.org/10.5220/0013819000003985

    work page doi:10.5220/0013819000003985 2025

  4. [4]

    Jacobs, S

    Brockenbrough, A., Salinas, D.: Using generative ai to create user stories in the software engineering classroom. In: Proc. of the 36th Intnl. Con- ference on Software Engineering Education and Training. pp. 1–5 (2024). https://doi.org/10.1109/CSEET62301.2024.10662994

    work page doi:10.1109/cseet62301.2024.10662994 2024

  5. [5]

    experiences from a teaching perspective

    Chan, M.M., Amado-Salvatierra, H.R., Hernandez-Rizzardini, R., De La Roca, M.: The potential role of ai-based chatbots in engineering education. experiences from a teaching perspective. In: Proc. of the Frontiers in Education Conference. pp. 1–5 (2023). https://doi.org/10.1109/FIE58773.2023.10343296

    work page doi:10.1109/fie58773.2023.10343296 2023

  6. [6]

    IEEE Access8, 75264–75278 (2020)

    Chen, L., Chen, P., Lin, Z.: Artificial intelligence in education: A review. IEEE Access8, 75264–75278 (2020). https://doi.org/10.1109/ACCESS.2020.2988510

    work page doi:10.1109/access.2020.2988510 2020

  7. [7]

    In: Proc

    Cruzes, D.S., Dyba, T.: Recommended steps for thematic synthesis in software engineering. In: Proc. of the Intnl. Symposium on Empirical Software Engineering and Measurement. p. 275–284. IEEE (2011), http://hdl.handle.net/10125/79444 24 M. Neumann et al

    work page 2011

  8. [8]

    Jacobs, S

    Datta, S.: Using generative artificial intelligence tools in software engineering courses. In: Proc. of the 36th Intnl. Conf. on Software Engineering Education and Training. pp. 1–2 (2024). https://doi.org/10.1109/CSEET62301.2024.10663042

    work page doi:10.1109/cseet62301.2024.10663042 2024

  9. [9]

    In: Proc

    Freise, L.R., Bruhin, O., Ritz, E., Li, M.M., Leimeister, J.M.: Code and craft: How generative ai tools facilitate job crafting in software develop- ment. In: Proc. of the 58th Hawaii Intnl. Conf. on System Sciences (2025), https://hdl.handle.net/10125/109683

    work page 2025

  10. [10]

    In: Wirtschaftsinformatik2024Proceedings(2024),https://aisel.aisnet.org/wi2024/24

    Förster, M., Pitz, K., Wrabel, A., Klier, M., Zimmermann, S.: Building ai literacy with experiential learning – insights from a field experiment in k-12 education. In: Wirtschaftsinformatik2024Proceedings(2024),https://aisel.aisnet.org/wi2024/24

    work page 2024

  11. [11]

    von Garrel, J., Mayer, J., Mühlfeld, M.: Künstliche intelligenz im studium eine quantitative befragung von studierenden zur nutzung von Chat- GPT & co. (2023). https://doi.org/10.48444/H_DOCS-PUB-395, https://opus4.kobv.de/opus4-h-da/395

    work page doi:10.48444/h_docs-pub-395 2023

  12. [12]

    https://doi.org/DOI: 10.3278/HSL2411W

    Gottschling, S., Seidl, T., Vonhof, C.: Nutzung von KI-tools durch studierende (11) (2024). https://doi.org/DOI: 10.3278/HSL2411W

    work page doi:10.3278/hsl2411w 2024

  13. [13]

    In: ICIS 2024 Proceedings (2024), https://aisel.aisnet.org/icis2024/aiinbus/aiinbus/20

    Grashoff, I., Mayer, T., Recker, J.: Challenges, success factors, and poten- tial benefits of genai development: Insights from an ongoing case study at a german automotive manufacturer. In: ICIS 2024 Proceedings (2024), https://aisel.aisnet.org/icis2024/aiinbus/aiinbus/20

    work page 2024

  14. [14]

    of the 12th Intnl

    Jimenez, C.E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., Narasimhan, K.R.: SWE-bench: Can language models resolve real-world github issues? In: Proc. of the 12th Intnl. Conf. on Learning Representations (2024)

    work page 2024

  15. [15]

    J Sci Teacher Educ26, 593—-597 (2015)

    Lederman, N., Lederman, J.: What is a theoretical framework? a practical answer. J Sci Teacher Educ26, 593—-597 (2015). https://doi.org/\url {10.1007/s10972- 015-9443-2 }

    work page doi:10.1007/s10972- 2015

  16. [16]

    Interna- tional Journal of Educational Technology in Higher Education21, 58 (2024)

    Mah, D.K., Groß, N.: Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs. Interna- tional Journal of Educational Technology in Higher Education21, 58 (2024). https://doi.org/10.1186/s41239-024-00490-1

    work page doi:10.1186/s41239-024-00490-1 2024

  17. [17]

    experiences from a teaching perspective

    Maher, M.L., Tadimalla, S.Y., Dhamani, D.: An exploratory study on the impact of ai tools on the student experience in programming courses: an intersectional analysis approach. In: Proc. of the Frontiers in Education Conference. pp. 1–5 (2023). https://doi.org/10.1109/FIE58773.2023.10343037

    work page doi:10.1109/fie58773.2023.10343037 2023

  18. [18]

    Matthies, C., Teusner, R., Perscheid, M.: Challenges (and opportunities!) of a re- moteagilesoftwareengineeringprojectcourseduringcovid-19.In:Proc.ofthe55th Hawaii Intnl. Conf. on System Sciences (2022), http://hdl.handle.net/10125/79444

    work page 2022

  19. [19]

    In: Proc

    Neumann, M., Mötefindt, D., Linke, L., Radtke, D., Mattstädt, A., Herzig, F., Regel, P.: How to fill the gap between practice and higher education: Performing eduscrum with real world problems in virtual distance teaching. In: Proc. of the 17th Intnl. Conf. on Software Engineering Advances. pp. 1–5 (2022)

    work page 2022

  20. [20]

    we need to talk about chat- gpt

    Neumann, M., Rauschenberger, M., Schön, E.M.: “we need to talk about chat- gpt”: The future of ai and higher education. In: Proc. of the 5th Intnl. Workshop on Software Engineering Education for the Next Generation. pp. 29–32 (2023). https://doi.org/10.1109/SEENG59157.2023.00010

    work page doi:10.1109/seeng59157.2023.00010 2023

  21. [21]

    SIGMIS Database55(1), 6–11 (Feb 2024)

    Nithithanatchinnapat, B., Maurer, J., Deng, X.N., Joshi, K.D.: Future busi- ness workforce: Crafting a generative ai-centric curriculum today for to- morrow’s business education. SIGMIS Database55(1), 6–11 (Feb 2024). https://doi.org/10.1145/3645057.3645059 A Systematic AI Adoption Framework for Higher Education 25

    work page doi:10.1145/3645057.3645059 2024

  22. [22]

    In:Marchesi,L.,Goldman,A.,Lunesu,M.I.,Przybyłek,A.,Aguiar,A.,Morgan,L., Wang, X., Pinna, A

    Rasheed, Z., Waseem, M., Sami, M.A., Kemell, K.K., Ahmad, A., Duc, A.N., Systä, K., Abrahamsson, P.: Autonomous agents in software development: A vision paper. In:Marchesi,L.,Goldman,A.,Lunesu,M.I.,Przybyłek,A.,Aguiar,A.,Morgan,L., Wang, X., Pinna, A. (eds.) Agile Processes in Software Engineering and Extreme Programming – Workshops. pp. 15–23. Springer N...

    work page 2025

  23. [23]

    New Zealand Jour- nal of Educational Studies (2025)

    Rizki, I.A., Daoud, R.: Generative artificial intelligence in higher education: Re- view of institutional policies and practices across new zealand. New Zealand Jour- nal of Educational Studies (2025). https://doi.org/10.1007/s40841-025-00417-y, https://doi.org/10.1007/s40841-025-00417-y

    work page doi:10.1007/s40841-025-00417-y 2025

  24. [24]

    In: Proceedings of the 15th International Learning Analytics and Knowledge Conference

    Rüdian, S., Podelo, J., Kužílek, J., Pinkwart, N.: Feedback on feedback: Stu- dent’s perceptions for feedback from teachers and few-shot llms. In: Proceedings of the 15th International Learning Analytics and Knowledge Conference. p. 82–92. LAK ’25, Association for Computing Machinery, New York, NY, USA (2025). https://doi.org/10.1145/3706468.3706479

    work page doi:10.1145/3706468.3706479 2025

  25. [25]

    Empirical Software Engineer- ing 14, 131–164

    Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empirical Software Engineering14(2), 131–164 (2009). https://doi.org/\url {10.1007/s10664-008-9102-8}

    work page doi:10.1007/s10664-008-9102-8 2009

  26. [26]

    In: Pfahl, D., Gonzalez Huerta, J., Klünder, J., Anwar, H

    Sami, M.A., Waseem, M., Zhang, Z., Rasheed, Z., Systä, K., Abrahamsson, P.: Early results of an ai multiagent system for requirements elicitation and analysis. In: Pfahl, D., Gonzalez Huerta, J., Klünder, J., Anwar, H. (eds.) Product-Focused Software Process Improvement. pp. 307–316. Springer Nature Switzerland, Cham (2025)

    work page 2025

  27. [27]

    of the Conf

    Savelka, J., Agarwal, A., Bogart, C., Song, Y., Sakr, M.: Can generative pre-trained transformers (gpt) pass assessments in higher education programming courses? In: Proc. of the Conf. on Innovation and Technology in Computer Science Education V. 1. p. 117–123. Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3587102.3588792

    work page doi:10.1145/3587102.3588792 2023

  28. [28]

    https://doi.org/10.3389/fcomp.2023.1208550

    Schön, E.M., Neumann, M., Hofmann-Stölting, C., Baeza-Yates, R., Rauschen- berger,M.:Howareaiassistantschanginghighereducation?FrontiersinComputer Science5(2023). https://doi.org/10.3389/fcomp.2023.1208550

    work page doi:10.3389/fcomp.2023.1208550 2023

  29. [29]

    In: Marchiori, M., Domínguez Mayo, F.J., Filipe, J

    Schön, E.M., Buchem, I., Sostak, S., Rauschenberger, M.: Shift toward value- based learning: Applying agile approaches in higher education. In: Marchiori, M., Domínguez Mayo, F.J., Filipe, J. (eds.) Web Information Systems and Technolo- gies. pp. 24–41. Springer Nature Switzerland, Cham (2023)

    work page 2023

  30. [30]

    IEEE Transactions on Education67(5), 777–785 (2024)

    Shailendra, S., Kadel, R., Sharma, A.: Framework for adoption of generative ar- tificial intelligence (genai) in education. IEEE Transactions on Education67(5), 777–785 (2024). https://doi.org/10.1109/TE.2024.3432101

    work page doi:10.1109/te.2024.3432101 2024

  31. [31]

    Computers and Education: Artificial Intelligence4, 100127 (2023)

    Southworth, J., Migliaccio, K., Glover, J., Glover, J., Reed, D., McCarty, C., Brendemuhl, J., Thomas, A.: Developing a model for ai across the cur- riculum: Transforming the higher education landscape via innovation in ai literacy. Computers and Education: Artificial Intelligence4, 100127 (2023). https://doi.org/https://doi.org/10.1016/j.ca eai.2023.100127

    work page doi:10.1016/j.ca 2023

  32. [32]

    In: Proc

    Speth, S., Meißner, N., Becker, S.: Investigating 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. pp. 142–146 (2023). https://doi.org/10.1109/CSEET58097.2023.00030

    work page doi:10.1109/cseet58097.2023.00030 2023

  33. [33]

    Jacobs, S

    Speth, S., Meißner, N., Becker, S.: Chatgpt’s aptitude in utilizing uml di- agrams for software engineering exercise generation. In: Proc. of the 36th Intnl. Conf. on Software Engineering Education and Training. pp. 1–5 (2024). https://doi.org/10.1109/CSEET62301.2024.10663027 26 M. Neumann et al

    work page doi:10.1109/cseet62301.2024.10663027 2024

  34. [34]

    ECNU Rev

    Su, J., Yang, W.: Unlocking the power of chatgpt: A framework for applying gen- erative ai in education. ECNU Rev. Educ.6(3), 355–366 (2023)

    work page 2023

  35. [35]

    Technology, Knowledge and Learning30, 1825–1846 (2025)

    Wang, C.: Exploring students’ generative ai-assisted writing processes: Percep- tions and experiences from native and nonnative english speakers. Technology, Knowledge and Learning30, 1825–1846 (2025). https://doi.org/10.1007/s10758- 024-09744-3, https://doi.org/10.1007/s10758-024-09744-3

    work page doi:10.1007/s10758- 2025

  36. [36]

    Computers and Education: Artificial Intelligence 8, 100405 (2025)

    Wiese, L.J., Patil, I., Schiff, D.S., Magana, A.J.: Ai ethics education: A systematic literature review. Computers and Education: Artificial Intelligence 8, 100405 (2025). https://doi.org/https://doi.org/10.1016/j.caeai.2025.100405, https://www.sciencedirect.com/science/article/pii/S2666920X25000451

    work page doi:10.1016/j.caeai.2025.100405 2025

  37. [37]

    Yin, R.K.: Case study research: Design and methods, Applied social research meth- ods series, vol. 5. Sage, 4th edn. (2009)

    work page 2009

  38. [38]

    experiences from a teaching perspective

    Zastudil, C., Rogalska, M., Kapp, C., Vaughn, J., MacNeil, S.: Gener- ative ai in computing education: Perspectives of students and instruc- tors. In: Proc. of the Frontiers in Education Conference. pp. 1–9 (2023). https://doi.org/10.1109/FIE58773.2023.10343467

    work page doi:10.1109/fie58773.2023.10343467 2023

  39. [39]

    In: Ag- ile Processes in Software Engineering and Extreme Programming

    Zhang,Z.,Rayhan,M.,Herda,T.,Goisauf,M.,Abrahamsson,P.:Llm-basedagents for automating the enhancement of user story quality: An early report. In: Ag- ile Processes in Software Engineering and Extreme Programming. pp. 117–126. Springer Nature Switzerland, Cham (2024)

    work page 2024