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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.