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AI-Based Adaptive Feedback in Simulations for Teacher Education: An Experimental Replication in the Field

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Title: AI-Based Adaptive Feedback in Simulations for Teacher Education: An Experimental Replication in the Field
Language: English
Authors: Elisabeth Bauer (ORCID 0000-0003-4078-0999), Michael Sailer, Frank Niklas, Samuel Greiff, Sven Sarbu-Rothsching, Jan M. Zottmann, Jan Kiesewetter, Matthias Stadler (ORCID 0000-0001-8241-8723), Martin R. Fischer, Tina Seidel, Detlef Urhahne, Maximilian Sailer, Frank Fischer
Source: Journal of Computer Assisted Learning. 2025 41(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 20
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing, Automation, Formative Evaluation, Preservice Teachers, Program Effectiveness, Online Courses, Foreign Countries, College Students
Geographic Terms: Germany
DOI: 10.1111/jcal.13123
ISSN: 0266-4909
1365-2729
Abstract: Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks enhanced preservice teachers' diagnostic reasoning in a digital case-based simulation. However, the effectiveness of the simulation with the different feedback types and the generalizability to field settings remained unclear. Objectives: We tested the generalizability of the previous findings and the effectiveness of a single simulation session with either feedback type in an experimental field study. Methods: In regular online courses, 332 preservice teachers at five German universities participated in one of three randomly assigned groups: (1) a simulation group with NLP-based adaptive feedback, (2) a simulation group with static feedback and (3) a no-simulation control group. We analysed the effect of the simulation with the two feedback types on participants' judgement accuracy and justification quality. Results and Conclusions: Compared with static feedback, adaptive feedback significantly enhanced justification quality but not judgement accuracy. Only the simulation with adaptive feedback significantly benefited learners' justification quality over the no-simulation control group, while no significant differences in judgement accuracy were found. Our field experiment replicated the findings of the laboratory study. Only a simulation session with adaptive feedback, unlike static feedback, seems to enhance learners' justification quality but not judgement accuracy. Under field conditions, learners require adaptive support in simulations and can benefit from NLP-based adaptive feedback using artificial neural networks.
Abstractor: As Provided
Notes: https://osf.io/hn7wm
Entry Date: 2025
Accession Number: EJ1459037
Database: ERIC
FullText Text:
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  Data: AI-Based Adaptive Feedback in Simulations for Teacher Education: An Experimental Replication in the Field
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Elisabeth+Bauer%22">Elisabeth Bauer</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4078-0999">0000-0003-4078-0999</externalLink>)<br /><searchLink fieldCode="AR" term="%22Michael+Sailer%22">Michael Sailer</searchLink><br /><searchLink fieldCode="AR" term="%22Frank+Niklas%22">Frank Niklas</searchLink><br /><searchLink fieldCode="AR" term="%22Samuel+Greiff%22">Samuel Greiff</searchLink><br /><searchLink fieldCode="AR" term="%22Sven+Sarbu-Rothsching%22">Sven Sarbu-Rothsching</searchLink><br /><searchLink fieldCode="AR" term="%22Jan+M%2E+Zottmann%22">Jan M. Zottmann</searchLink><br /><searchLink fieldCode="AR" term="%22Jan+Kiesewetter%22">Jan Kiesewetter</searchLink><br /><searchLink fieldCode="AR" term="%22Matthias+Stadler%22">Matthias Stadler</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8241-8723">0000-0001-8241-8723</externalLink>)<br /><searchLink fieldCode="AR" term="%22Martin+R%2E+Fischer%22">Martin R. Fischer</searchLink><br /><searchLink fieldCode="AR" term="%22Tina+Seidel%22">Tina Seidel</searchLink><br /><searchLink fieldCode="AR" term="%22Detlef+Urhahne%22">Detlef Urhahne</searchLink><br /><searchLink fieldCode="AR" term="%22Maximilian+Sailer%22">Maximilian Sailer</searchLink><br /><searchLink fieldCode="AR" term="%22Frank+Fischer%22">Frank Fischer</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2025 41(1).
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: Y
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  Data: 20
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  Data: 2025
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  Data: Journal Articles<br />Reports - Research
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  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Artificial+Intelligence%22">Artificial Intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Simulation%22">Computer Simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+Language+Processing%22">Natural Language Processing</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Formative+Evaluation%22">Formative Evaluation</searchLink><br /><searchLink fieldCode="DE" term="%22Preservice+Teachers%22">Preservice Teachers</searchLink><br /><searchLink fieldCode="DE" term="%22Program+Effectiveness%22">Program Effectiveness</searchLink><br /><searchLink fieldCode="DE" term="%22Online+Courses%22">Online Courses</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Germany%22">Germany</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/jcal.13123
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks enhanced preservice teachers' diagnostic reasoning in a digital case-based simulation. However, the effectiveness of the simulation with the different feedback types and the generalizability to field settings remained unclear. Objectives: We tested the generalizability of the previous findings and the effectiveness of a single simulation session with either feedback type in an experimental field study. Methods: In regular online courses, 332 preservice teachers at five German universities participated in one of three randomly assigned groups: (1) a simulation group with NLP-based adaptive feedback, (2) a simulation group with static feedback and (3) a no-simulation control group. We analysed the effect of the simulation with the two feedback types on participants' judgement accuracy and justification quality. Results and Conclusions: Compared with static feedback, adaptive feedback significantly enhanced justification quality but not judgement accuracy. Only the simulation with adaptive feedback significantly benefited learners' justification quality over the no-simulation control group, while no significant differences in judgement accuracy were found. Our field experiment replicated the findings of the laboratory study. Only a simulation session with adaptive feedback, unlike static feedback, seems to enhance learners' justification quality but not judgement accuracy. Under field conditions, learners require adaptive support in simulations and can benefit from NLP-based adaptive feedback using artificial neural networks.
– Name: AbstractInfo
  Label: Abstractor
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  Data: As Provided
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  Data: https://osf.io/hn7wm
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  Data: 2025
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  Data: EJ1459037
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      – Text: English
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      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Feedback (Response)
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      – SubjectFull: Computer Simulation
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      – SubjectFull: Natural Language Processing
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      – SubjectFull: Preservice Teachers
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      – SubjectFull: Program Effectiveness
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      – SubjectFull: Online Courses
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