Choose one track. Master’s proposals target Spring 2027. Final scope and programme approval are agreed with the supervisor; bachelor scopes are suggested adaptations.
TRACK 01 · IMPLEMENTATION INCLUDED
Technical track
Develop a working solution and test whether it addresses the problem.
Possible research question
How can non-specialists build small applications with AI while retaining clear requirements, enforceable constraints and a viable handover?
Suggested tasks
Read research on AI-assisted end-user development and compare existing solutions.
Identify one problem faced by non-specialist app creators and IT staff.
Write a research question and define what the solution should do.
Choose one small workflow, such as submitting and approving a request.
Build an AI app generator with explicit fields, access rules and checks. Let users inspect and correct the generated app in an isolated test environment.
Compare it with the same AI model generating an app without these rules. Check requirements, access mistakes and the effort needed to fix the app.
Explain what worked, what did not, and how the results compare with earlier research.
Evaluation, degree scope and deliverables
Study and evaluation
Compare the implemented solution with the same model generating an unconstrained prototype. Combine reproducible technical tests with an appropriate empirical evaluation.
Requirement satisfaction and blocked permission violations
Correction effort
Successful maintenance by a second participant
Degree scope
Bachelor’s
Build a generator for one simple approval workflow with schema validation and clear permissions. Evaluate correctness and a handover task.
Master’s
Study how constrained generation affects end-user control, governance and maintainability compared with unconstrained generation.
Background
Web or backend development
Data modelling
Interest in requirements and user testing
Possible deliverables
A focused literature review, justified problem and research question
A working prototype with source code and setup instructions
A reproducible comparison and an appropriate study of use
A report explaining design lessons, results and limitations
TRACK 02 · NO IMPLEMENTATION REQUIRED
Non-technical track
Study existing systems, information or work practices. You do not need to develop software.
Possible research question
How do organisations govern AI-assisted application creation by non-specialists?
Suggested tasks
Read earlier studies of AI-assisted end-user development.
Choose one problem and write a research question the study can answer.
Interview end users and IT staff about ownership, permissions and support responsibilities.
Study an existing small-app workflow or a fictional request-and-approval case.
Analyse how requirements, quality checks and handover practices affect maintainability and local innovation.
Analyse the interviews, observations or documents using a clearly described method. Look for disagreements as well as common patterns.
Explain the findings, compare them with earlier research and suggest practical improvements.
Evaluation, degree scope and deliverables
Study and evaluation
Use a bounded empirical study of AI-assisted end-user development. Justify case selection, recruitment and the analysis method. Distinguish observed behaviour from participants’ perceptions; use triangulation or a comparison where it serves the research question.
Governance tensions and responsibility gaps
Conditions for a usable handover and sustainable adoption
Evidence for the findings, conflicting cases and limits of the study
Degree scope
Bachelor’s
Study one case or a small set of existing materials. Agree the interviews, documents or scenario tasks with the supervisor. Describe the method, analyse the findings and give practical recommendations.
Master’s
Use a clear research question and relevant IS theory. Justify the cases, participants and analysis method. Explain what the findings add to earlier research and where they may apply. No software development is required.
Background
Literature review and academic writing
Qualitative or quantitative research methods
Interest in AI-assisted end-user development; no programming prerequisite
Possible deliverables
A literature review and research question
A study plan and approved research material
An analysis supported by interviews, observations, documents or scenario results
A thesis with findings, recommendations and limitations
Scope and access
One application family and artificial organisational data. Do not aim for a general-purpose low-code platform. These implementation-related limits apply when developing or testing a technical solution. For a non-technical study, agree access to participants or existing materials early, use approved or fictional cases where appropriate, and distinguish perceptions from observed outcomes.
Agree access to data, participants or existing materials and any required ethics or privacy review before committing. A non-technical track needs a systematic study, not a working prototype.
Full academic proposal
Working topic
Governed AI-Assisted Development of Small Organisational Applications
Brief outline
This proposal examines AI-assisted end-user development in the work and information needs of non-specialist app creators and IT staff. The technical track combines a literature review and justified gap with requirements, design, implementation and evaluation of a bounded solution. The non-technical track investigates practices, experiences or organisational conditions through a systematic study of existing systems, documents or scenarios, without requiring implementation. Choose one track and agree the final research question, degree scope and contribution with the supervisor.
Programme fit
Information Systems. These are suggested research approaches, not a statement of confirmed programme policy. Agree the final title, track, degree scope and contribution with the supervisor and programme.
Shared research foundation
Review the literature; identify and justify a gap; formulate research questions; conduct a systematic study; analyse the evidence; explain the contribution relative to prior research and discuss limitations. The technical track additionally includes requirements, design, implementation and evaluation of an artifact.
