Color me confoundedA critical analysis of media comparisons on ChatGPT in education
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Zusammenfassungen
Research on the use of artificial intelligence in education is rapidly accumulating. In the ChatGPT literature, two recent meta-analyses were published (i.e., Deng et al., 2025 and Wang & Fan, 2025) that examined media comparisons between ChatGPT and more traditional forms of instruction; both reviews found ChatGPT to improve learning. However, as researchers have found in media comparisons studies, instructional methods are often not controlled between conditions, making it difficult to attribute any learning differences to affordances of the medium itself. Therefore, the purpose of our critical review was to examine those media comparisons studies included in Deng et al. (2025) and Wang and Fan (2025) to understand whether the ChatGPT and control conditions were comparable on their instructional features (i.e., matched on instructional methods, practice with the dependent measure, and time spent learning the content). Results indicated a lack of control in the ChatGPT studies that were reviewed, with many studies conflating the use of ChatGPT with other instructional features. Further, across the various direct learning outcomes, studies often did not include enough information to determine the comparability of conditions. For the most common learning outcome (i.e., ChatGPT > Control), comparisons often involved a confound between conditions as well as missing information about instructional features. Therefore, the benefits of ChatGPT must be interpreted with caution at the present time, and more research is needed to determine how, for whom, and under what pedagogical conditions ChatGPT can improve learning.
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![]() Personen KB IB clear | Maria Bannert , Hamsa Bastani , Osbert Bastani , Richard E. Clark , Daryna Dementieva , Ruiqi Deng , Hendrik Drachsler , Wenxiang Fan , Frank Fischer , Dragan Gašević , Urs Gasser , Haosen Ge , Georg Groh , Stephan Günnemann , Eyke Hüllermeier , Maoli Jiang , Özge Kabakcı , Gjergji Kasneci , Enkelejda Kasneci , Paul A. Kirschner , Robert B. Kozma , Stephan Krusche , Stefan Küchemann , Jochen Kuhn , Gitta Kutyniok , Shasha Liu , Yuyan Lu , Rei Mariman , Tilman Michaeli , Claudia Nerdel , Jürgen Pfeffer , Oleksandra Poquet , Justin Reich , Michael Sailer , Albrecht Schmidt , Tina Seidel , Kathrin Sessler , Matthias Stadler , Alp Sungu , Jin Wang , Joshua Weidlich , Jochen Weller , Xinlu Yu | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
![]() Begriffe KB IB clear |
Chat-GPT
, Generative Machine-Learning-Systeme (GMLS) computer-generated text
, GMLS & Bildung
, Künstliche Intelligenz (KI / AI) artificial intelligence
, Metaanalysemeta-analysis
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Dieser wissenschaftliche Zeitschriftenartikel erwähnt vermutlich nicht ... 
![]() Nicht erwähnte Begriffe | Effektstärke, Generative Pretrained Transformer 3 (GPT-3), Generative Pretrained Transformer 4 (GPT-4), GMLS & Hochschule, GMLS & Schule |
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Chat-GPT
Generative Machine-Learning-Systeme (GMLS)
Künstliche Intelligenz (KI / AI)








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