During this minor students will work on a realworld business challenge where AI can provide added value.
✅ Key features of the challenge
Programme: Informatics / Information Technology
Student level: Third-year Bachelor's students
Team size: 4–5 students
Student workload: Approximately 14 hours per student per week
Project duration: 20 weeks (February – June 2027)
Supervision: Bi-weekly meetings with lecturers and regular contact with the company supervisor
Expected company involvement: Approximately 0.5–1 hour per week
Language: English
Location: Inholland University of Applied Sciences, Haarlem
Matching: free of charge
About the project
Project Data & AI is part of the Data & AI minor. At the start of the minor, students choose from a range of projects proposed by external companies and organisations. Each project addresses a real-world business challenge where Artificial Intelligence can provide added value.
Together with the company, students further refine the problem definition, analyse the available data, investigate possible AI approaches, and develop a proof-of-concept solution. Throughout the project, students work closely with the company supervisor to validate ideas, discuss progress, and ensure that the proposed solution remains relevant to the business needs.
Depending on the nature of the challenge, students may develop solutions using machine learning, deep learning, computer vision, natural language processing, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multimodal AI, or other appropriate AI techniques.
The project concludes with a final presentation in which students demonstrate their proof-of-concept application and discuss the obtained results.
Data requirements
The project runs from February until June 2027.
The company is expected to provide a relevant dataset or other data source that enables students to investigate the proposed research question. Depending on the project, this may consist of images, videos, tabular data, sensor measurements, text documents, audio recordings, or other suitable data sources.
The dataset does not need to be perfectly cleaned or complete, but it should be sufficiently representative to support a 20-week student project. If the available data is limited, we will gladly discuss whether the challenge is suitable for the minor.
The dataset should be available for review before the collaboration is confirmed. Any privacy, confidentiality, or GDPR-related restrictions should also be discussed beforehand.
Deliverables
During the project, students will produce:
Literature review and problem analysis
Data exploration and preprocessing
AI solution with a well-justified technical approach
Proof-of-concept application demonstrating the solution
Technical design documentation
Final report
Final presentation
The developed application serves as a proof of concept to demonstrate the technical feasibility of the proposed AI solution. It is not intended to be a production-ready system.
📑Example challenges
Examples of suitable projects include:
Automatic document classification or information extraction
AI assistant based on company documentation
Computer vision for quality inspection or object detection
Predicting demand, maintenance, or production outcomes
Forecasting visitor numbers or sales
Detecting anomalies in sensor or operational data
Matching candidates to vacancies or projects
Analysing audio or video recordings using AI
Medical image or signal analysis
Generating synthetic data for privacy-sensitive applications
Criteria
A suitable challenge should meet the following criteria:
A clear business problem that can realistically be addressed using AI.
A suitable dataset or data source that allows students to investigate the research question.
A company supervisor who is available for consultation (approximately 0.5–1 hour per week).
Availability of the company supervisor during the final presentations in June 2027.
Communication with students can take place in English.
The challenge offers sufficient scope for students to explore, experiment, and evaluate different AI approaches.
🗓Planning
Early February
Students rank their preferred projects.
Lecturers assign teams to projects.
Mid February
Kick-off meeting between students and company (preferably at the company location).
Students further define the project scope together with the company.
February – June
Regular meetings between students and the company supervisor.
Iterative development of the AI solution.
March
Deliverable A: Literature review and data exploration.
April
Deliverable B: AI experiments and evaluation of alternative approaches.
May
Deliverable C: Technical design and proof-of-concept implementation.
June
Deliverables D & E: Final proof-of-concept application, final report, and presentation.
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