Teaching Day 2026

 StudyShorts is an AI platform we developed as a start-up idea, which converts educational content into 60-second videos.

The tool will be released as a beta version in mid-March, providing initial data on user uptake and learning effectiveness.

Potential uses: exam preparation, the flipped classroom, self-study and knowledge consolidation. 

Anja Pfaffinger (TH Ingolstadt)

AI@ProSuccess: AI as a booster for part-time study

How can lecture notes be transformed into compact learning nuggets using AI? The poster session demonstrates how, at the THI Campus for Continuing Education, AI-based microlearning formats make part-time study more flexible and support teaching staff in the didactic preparation of course materials.

Andrea Zott, Sebastian Vauth (TH Ingolstadt)

The Teaching Library integrates AI tools for literature research and topic structuring into a school module for Year 11 pupils, as well as into the assessment format of an advanced course for the School of Computer Science.

Practical application and reflection highlight the opportunities and limitations of AI-supported processes.

Dr. Beatrice Baldarelli (TH Ingolstadt)

Scalable AI teaching: 95% cache efficiency on EPYC-based VMs
Project ALICE offers CPU inference on VMs. A pilot study (n > 120) demonstrates 95% cache efficiency, including AI-generated cache. The poster presents hybrid prompt matching and demonstrates, using the Interactive Response Time Law, that a 
capacity of 650 students per VM (4 vCPUs, 8 GB RAM) on the Milan baseline 
can be served stably. Benchmarks on latency and cost-effectiveness for scaled operation are presented

Andreas Lindner  (TH Deggendorf)

ProgressLens is a project designed to automatically analyse handwritten solutions to maths problems, with the aim of combining personalised feedback with automated marking. To achieve this, multimodal language models such as GPT, Claude and Gemini are used to evaluate scanned solution pages, taking into account the problem statement and a marking scheme. The focus is on supporting learning progress through timely, targeted feedback that highlights knowledge gaps and identifies specific steps for improvement. Initial results on three exam datasets are promising overall.

Prof. Dr. Ulrich Schlickewei (TH Ingolstadt)

Subject-specific AI learning partners (RAG/SLM) in the ALICE project, providing prompts, discussion and reflection:

ALICE uses SLMs for local CPU inference without the need for expensive hardware. Through 
course-specific RAG layers and active prompt caching, the learning partner is 
restricted to lecture content and isolated from background knowledge in order to minimise hallucinations.

Andreas Lindner (TH Deggendorf)

"Societal Challenges of AI: Approaches to Solutions in Everyday University Life"

How can universities tackle the ethical challenges of AI in practical terms in their day-to-day operations?
This interactive workshop invites participants to identify their most pressing AI issues
in a practical manner. Concrete ideas for action will be developed as prototypes and presented in an
“elevator pitch”.

The workshop creates a safe space for critical thinking
and practical approaches to responsible AI in higher education. The concept was
developed and has already been tested as part of the HFD’s AI Lab.

Petra Amasreiter (TH Rosenheim)

How does the use of generative AI tools influence learning, motivation and self-regulation? This 90-minute workshop deliberately focuses on the students’ perspective. Drawing on media psychology theories, participants will explore the key opportunities and challenges of AI-supported learning through interactive co-creation formats. The aim is to generate ideas for teaching that takes students’ actual needs into account.

Prof. Dr Christine Hennighausen (TH Ingolstadt)

Escape the Bias is an interactive escape game exploring AI, fairness, and hidden decision-making, currently in development. Participants step into a company where an AI system, originally designed for hiring, is repurposed to evaluate existing employees. What appears to be an objective, data-driven process gradually reveals hidden biases embedded in proxy variables and historical data. Players uncover how design choices, optimization goals, and fairness metrics can create unintended distortions. Through puzzles and time pressure, they reconstruct how the system works and critically reflect on its implications -  culminating in the question: can an algorithm ever be truly fair? The workshop offers insights into game-based learning and early impressions of the game’s design.

Prof. Dr Laura Bechthold, Julia Ruttmann (TH Ingolstadt)

A workshop by students for students of all disciplines

In this two-part module for STEM and Business students, we will explore how language models (LLMs) work and how they are used. We will examine the process from training data to the model, clarify legal aspects, and practise strategic prompting in an academic context. A key focus will be on critically evaluating results: what are the limitations of the technology, and how can we identify errors or bias?

We would like to offer this as a two-part series, and Parts 1 and 2 can be attended independently or together.

Part 1 focuses on the basics and functions (understanding the technology itself)

Part 2 focuses on prompting and data protection (critical engagement)

Studuierendenvertretung (TH Ingolstadt)

AI-powered microlearning content: Opportunities and challenges for higher education teaching, 
using StudyShorts as an example

The workshop addresses the discrepancy between the focus on social media and
formal learning readiness through an analysis of the AI app StudyShorts. During the workshop sessions,
participants will develop scenarios for didactic integration, evaluate learning effectiveness
versus superficiality, and establish criteria for responsible use.

Anja Pfaffinger (TH Ingolstadt)

Bavarian Foresight Institute – People on the Move

The increasing integration of AI into higher education and the workplace requires students to develop technological, critical-reflective and ethical skills. Using a real-world case study from university teaching, we will explore this topic to facilitate joint analysis and the development of ideas. 
The workshop combines insights with resources from the website zukunftsnavigator.info – in particular the Trend Navigator – and offers interactive working sessions in which participants develop concrete approaches to promoting AI literacy and future skills.

Dr. Gerhard Schönhofer (TH Ingolstadt)

Beyond Note-Taking: A local and transparent AI pipeline for marking oral
exams, which generates structured
feedback and marking suggestions based on audio recordings and course-related materials. 

Maximilian Dauner (HS München)

AI as an efficiency booster for lecturers: Optimise emails, feedback and Moodle. Use AI for assessment reports, exam questions and interactive simulations. This practical workshop shows you how to save time and further improve the quality of your teaching using AI, without generating ‘AI slop’.

Prof. Dr.-Ing. Hans-Joachim Hof (TH Ingolstadt)

A workshop by students for students of all disciplines

In this two-part module for STEM and Business students, we will explore how language models (LLMs) work and how they are used. We will examine the process from training data to the model, clarify legal aspects, and practise strategic prompting in an academic context. A key focus will be on critically evaluating results: what are the limitations of the technology, and how can we identify errors or bias?

We would like to offer this as a two-part series, and Parts 1 and 2 can be attended independently or together.

Part 1 focuses on the basics and functions (understanding the technology itself)

Part 2 focuses on prompting and data protection (critical engagement)

Studierendenvertretung (TH Ingolstadt)