01
Material becomes usable.
Existing content remains the core: courses, assignments, videos, texts and institutional knowledge.
Field: Education
Change teaching and learning with AI.
NSC connects materials, learning processes and reflections so educational institutions can understand how people learn, and build better learning and education environments from that insight.
Foundation
Education needs more than a secure chat.
Materials
Courses, videos, assignments and existing content become the foundation on which AI can work.
Learning processes
With Reson8, learners can be interviewed, accompanied and supported in their reflection, instead of only receiving ready-made answers.
Research
Conversations, working paths and reflections become material from which teaching and research can learn.
Working method
Content becomes learning processes. Learning processes become insight.
NSC does not only build an interface for AI. We connect material, assignments, conversations and reflections into an environment that supports teaching and makes learning understandable.
02
Learning becomes visible.
Reson8 can interview learners, ask follow up questions and capture reflections without reducing learning to clicks.
03
Teaching becomes connected.
From material and learning processes come assistants, learning paths, assignments and support for real educational situations.
04
Insights flow back.
What students need, where they get stuck and how they think becomes usable for teaching, research and transfer.
Learning and education environment
MaterialWhat are people working with?
ReflectionHow do learners think?
TeachingWhat helps in the next step?
ResearchWhat can be learned from this?
AI supports teaching, learning, reflection and research.
Practice
UnicornAI: a dedicated AI environment for education and teaching.
UnicornAI is a dedicated AI platform of the Center for Media Education at the University of Education Schwäbisch Gmünd. It brings together secure access, courses, learning paths, perspective shifts and custom assistants for schools, seminars and continuing education. The institution's material remains the core, and the environment can continue to grow with teaching, research and transfer.
From our research and practice
Pedagogical quality deserves scrutiny.
What values does a model express, how do its decisions differ, and who can use it meaningfully? These questions connect our research with developing AI for education.
Research contribution · 2026
Which values guide AI?
This chapter connects educational values with a systematic examination of AI decisions. It informs our approach to assessing whether AI fits its context through concrete situations and explicit criteria.
The Pedagogical Stance of AI
Daniel Autenrieth
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Oxford Intersections: AI in Society · 2026Empirical study · 2026
Does every model fit every learning task?
A comparison of two language models examines differences in their educational preferences. The study shows why model selection should also consider pedagogical orientation.
Different Models, Different Values: Educational Preference Structures across Alignment Methodologies in Large Language Models
Daniel Autenrieth
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CSEDU · Proceedings, Vol. 1 · 2026Conceptual contribution · 2026
Who can actually make use of AI?
Access to a tool alone does not ensure participation. This article examines barriers to AI use in education, focusing on capabilities, support and differences in people's starting conditions.
Hidden barriers of education and artificial intelligence: rethinking equity, affordances and ability
Daniel Autenrieth, Jan-René Schluchter, Lea Schulz
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Artificial Intelligence in Education · 2(5) · 2026
Next step