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Research on diagnosing in teacher education has primarily emphasized the accuracy of diagnostic judgments and has explained it in terms of factors such as diagnostic knowledge. However, approaches to scientific argumentation and information processing suggest differentiating between diagnostic judgment and diagnostic argumentation: When making accurate diagnostic judgments, the underlying reasoning can remain intuitive, whereas diagnostic argumentation requires controlled and explicable reasoning about a diagnostic problem to explain the reasoning in a comprehensible and persuasive manner. We suggest three facets of argumentation for conceptualizing diagnostic argumentation, which are yet to be addressed in teacher education research: justification of a diagnosis with evidence, disconfirmation of differential diagnoses, and transparency regarding the processes of evidence generation. Therefore, we explored whether preservice teachers’ diagnostic argumentation and diagnostic judgment might represent different diagnostic skills. We also explored whether justification, disconfirmation, and transparency should be considered distinct subskills of preservice teachers’ diagnostic argumentation. We reanalyzed data of 118 preservice teachers who learned about students’ learning difficulties with simulated cases. For each student case, the preservice teachers had to indicate a diagnostic judgment and provide a diagnostic argumentation. We found that preservice teachers’ diagnostic argumentation seldom involved all three facets, suggesting a need for more specific training. Moreover, the correlational results suggested that making accurate diagnostic judgments and formulating diagnostic argumentation may represent different diagnostic skills and that justification, disconfirmation, and transparency may be considered distinct subskills of diagnostic argumentation. The introduced concepts of justification, disconfirmation, and transparency may provide a starting point for developing standards in diagnostic argumentation in teacher education.

Recent advancements in artificial intelligence make its use in education more likely. In fact, existing learning systems already utilize it for supporting students’ learning or teachers’ judgments. In this perspective article, we want to elaborate on the role of humans in making decisions in the design and implementation process of artificial intelligence in education. Therefore, we propose that an artificial intelligence-supported system in education can be considered a closed-loop system, which includes the steps of (i) data recording, (ii) pattern detection, and (iii) adaptivity. Besides the design process, we also consider the crucial role of the users in terms of decisions in educational contexts: While some implementations of artificial intelligence might make decisions on their own, we specifically highlight the high potential of striving for hybrid solutions in which different users, namely learners or teachers, are provided with information from artificial intelligence transparently for their own decisions. In light of the non-perfect accuracy of decisions of both artificial intelligence-based systems and users, we argue for balancing the process of human- and AI-driven decisions and mutual monitoring of these decisions. Accordingly, the decision-making process can be improved by taking both sides into account. Further, we emphasize the importance of contextualizing decisions. Potential erroneous decisions by either machines or humans can have very different consequences. In conclusion, humans have a crucial role at many stages in the process of designing and using artificial intelligence for education.

In this article, we investigate diagnostic activities and diagnostic practices in medical education and teacher education. Previous studies have tended to focus on comparing knowledge between disciplines, but such an approach is complicated due to the content specificity of knowledge. We compared 142 learners from medical education and 122 learners from teacher education who were asked to (a) diagnose eight simulated cases from their respective discipline in a simulation-based learning environment and (b) write a justificatory report for each simulated case. We coded all justificatory reports regarding four diagnostic activities: generating hypotheses, generating evidence, evaluating evidence, and drawing conclusions. Moreover, using the method of Epistemic Network Analysis, we operationalized diagnostic practices as the relative frequencies of co-occurring diagnostic activities. We found significant differences between learners from medical education and teacher education with respect to both their diagnostic activities and diagnostic practices. Learners from medical education put relatively more emphasis on generating hypotheses and drawing conclusions, therefore applying a more hypothesis-driven approach. By contrast, learners in teacher education had a stronger focus on generating and evaluating evidence, indicating a more data-driven approach. The results may be explained by different epistemic ideals and standards taught in higher education. Further research on the issue of epistemic ideals and standards in diagnosing is needed. Moreover, we recommend that educators think beyond individuals’ knowledge and implement measures to systematically teach and increase the awareness of disciplinary standards.

Motor recovery following stroke is believed to necessitate alteration in functional connectivity between cortex and muscle. Cortico-muscular coherence has been proposed as a potential biomarker for post-stroke motor deficits, enabling a quantification of recovery, as well as potentially indicating the regions of cortex involved in recovery of function. We recorded simultaneous EEG and EMG during wrist extension from healthy participants and patients following ischaemic stroke, evaluating function at three time points post-stroke. EEG–EMG coherence increased over time, as wrist mobility recovered clinically, and by the final evaluation, coherence was higher in the patient group than in the healthy controls. Moreover, the cortical distribution differed between the groups, with coherence involving larger and more bilaterally scattered areas of cortex in the patients than in the healthy participants. The findings suggest that EEG–EMG coherence has the potential to serve as a biomarker for motor recovery and to provide information about the cortical regions that should be targeted in rehabilitation therapies based on real-time EEG.