Matthias Stadler

Dr. Matthias Stadler

orcid

Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 July 2024 in Computers in Human Behavior
Verified
Journal Article
Open access
MSMatthias Stadler
Matthias Stadler
MBMaria Bannert
Maria Bannert
MSMichael Sailer
Michael Sailer

30 January 2023 in EdArXiv
Verified
Preprint
Open access
EKEnkelejda Kasneci
Enkelejda Kasneci
KSKathrin Seßler
Kathrin Seßler
Stefan KüchemannStefan Küchemann
Stefan Küchemann
MBMaria Bannert
Maria Bannert
DDDaryna Dementieva
Daryna Dementieva
GKGjergji Kasneci
Gjergji Kasneci
Large language models represent a significant advancement in the field of AI. The underlying technology is key to further innovations and, despite critical views and even bans within communities and regions, large language models are here to stay. This position paper presents the potential benefits and challenges of educational applications of large language models, from student and teacher perspectives. We briefly discuss the current state of large language models and their applications. We then highlight how these models can be used to create educational content, improve student engagement and interaction, and personalize learning experiences. With regard to challenges, we argue that large language models in education require teachers and learners to develop sets of competencies and literacies necessary to both understand the technology as well as their limitations and unexpected brittleness of such systems. In addition, a clear strategy within educational systems and a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required to integrate and take full advantage of large language models in learning settings and teaching curricula. Other challenges such as the potential bias in the output, the need for continuous human oversight, and the potential for misuse are not unique to the application of AI in education. But we believe that, if handled sensibly, these challenges can offer insights and opportunities in education scenarios to acquaint students early on with potential societal biases, criticalities, and risks of AI applications. We conclude with recommendations for how to address these challenges and ensure that such models are used in a responsible and ethical manner in education.

1 June 2014 in Journal of Vocational Behavior
Verified
Journal Article
Closed access
HCHelena D. Cooper-Thomas
Helena D. Cooper-Thomas
NPNicole L. Paterson
Nicole L. Paterson
MSMatthias Stadler
Matthias Stadler
ASAlan M. Saks
Alan M. Saks

1 November 2015 in Intelligence
Verified
Journal Article
Closed access
MSMatthias Stadler
Matthias Stadler
NBNicolas Becker
Nicolas Becker
MGMarkus Gödker
Markus Gödker
DLDetlev Leutner
Detlev Leutner
SGSamuel Greiff
Samuel Greiff

26 February 2025 in npj Science of Learning
Verified
Journal Article
Open access
PDF file
Stefan KüchemannStefan Küchemann
Stefan Küchemann
KAKarina E. Avila
Karina E. Avila
YDYavuz Dinc
Yavuz Dinc
CHChiara Hortmann
Chiara Hortmann
NRNatalia Revenga
Natalia Revenga
Jochen KuhnJochen Kuhn
Jochen Kuhn
Recently, the option to use large language models as a middleware connecting various AI tools and other large language models led to the development of so-called large multimodal foundation models, which have the power to process spoken text, music, images and videos. In this overview, we explain a new set of opportunities and challenges that arise from the integration of large multimodal foundation models in education.

11 September 2014 in Thinking & Reasoning
Verified
Journal Article
Closed access
SGSamuel Greiff
Samuel Greiff
AFAndreas Fischer
Andreas Fischer
MSMatthias Stadler
Matthias Stadler
SWSascha Wüstenberg
Sascha Wüstenberg