TUHH: WG5

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WG 5 – Learning Booster for Students

What is it about?

The WG 5 develops AI-supported learning aids that specifically support students in their studies. AI assistants are intended to answer students’ questions, explain learning content in an understandable way and encourage critical engagement with the course content. The objective is to create innovative digital support that enables personalized learning and flexibly accompanies students in their everyday studies. WG 5 is also responsible for quality assurance and reflection on the use of AI assistants, with a focus on the student perspective.

If you have any questions or are interested, please contact Prof. Dr. Maren Baumhauer, Institute for Vocational Education and Digitalisation (T-EXK1), at: maren.baumhauer(at)tuhh(dot)de

Objectives & Approach

The work of WG 5 is divided into four phases: Phase 1 clarifies the understanding of the (technical) requirements and defines the development objectives. Phase 2 covers the iterative development and practical testing of AI-supported learning aids. Phase 3 comprises the evaluation of quality and acceptance as well as the derivation of optimization measures. Phase 4 focuses on potential expansion opportunities, the derivation of recommendations and consolidation. The phases are not to be understood as a linear sequence, but as overarching development sections within which repeated adaptation and reflection loops take place.

Phase 1: Concept and Requirements Phase

Define objectives and success criteria
Define quality requirements
Analyze technical and organizational framework conditions
Become familiar with the experimentation environment
Incorporate students’ needs (WG 6 survey results)
Prioritize use cases

Phase 2: Development and Testing Phase

Select and provide materials for the RAG system
Check the quality of the knowledge base
Test the quality of vectorization of the RAG system
Develop test scenarios and evaluation criteria
Iteratively develop and test system prompts
Create an accompanying concept for students
Create a data protection concept
Productive use with continuous optimization

Phase 3: Evaluation and Optimization Phase

Create an evaluation concept for productive use
Analyze usage data
Evaluate evaluation results
Conduct reflection workshops for students
Identify and prioritize needs for improvement

Phase 4: Expansion Phase

Evaluate usefulness and transferability (to further modules)
Derive recommendations
Decide on consolidation

Current: Pilot Project “AI Assistant for a Pilot Module”

The pilot project aims to design an AI assistant for a selected module that accompanies and supports students individually as a personal tutor while learning. The concept incorporates the specifics of the course, the needs of future users (interface with WG 6), and specific technical and organizational framework conditions (interface with WG 2) of Hamburg University of Technology. The development process is designed to be agile and includes iterative evaluation and improvement of the AI assistant during the pilot deployment in the winter semester 2026/27. Central elements of the pilot project also include a tailored didactic concept for integrating the AI assistant into teaching (interface with WG 4), an accompanying concept for students, and evaluation of the experiences with the AI assistant. The students’ perspective is a particular focus.

Leadership	Prof. Dr. Maren Baumhauer
Objective	AI-supported learning aids for students
Pilot project	AI Assistant for a Pilot Module
Timeline	August 2026 – January 2027