Holger Horz

Generative artificial intelligence (AI) shifts educational focus from access to information toward judging machine-generated claims and regulating the division of epistemic work. This conceptual article proposes the SAM model—self-regulation (S), ambiguity tolerance (A), and methodological competence (M)—as a bounded person-level resource model within broader AI readiness, with educators’ AI readiness as a central application. S denotes regulatory capacity, A a disposition-like willingness to tolerate unresolved epistemic ambiguity, and M the capacity to evaluate claims, differentiated into cross-domain M1 and domain-specific M2. SAM’s distinctive claim is configurational: M should predict independently assessed judgment more strongly at higher S and, under high ambiguity, when A is higher. These relations, the M1/M2 distinction, limited compensability, and incremental prediction beyond shared and neighboring accounts remain hypothetical. The model separates resources from enacted processes, contextual affordances, and outcomes and requires independent performance criteria. For AI-based assessment and learning analytics, educators’ interpretations of AI-derived evidence and construct inferences from behavioral traces require validated mappings addressing alternative explanations, comparability, fairness, and use. SAM thus offers a research agenda predicting when AI support preserves human judgment rather than merely improving supported performance.

Background: Blended care psychotherapy (BCP) combines face-to-face psychotherapy with digital interventions, such as videoconferencing, apps, or virtual reality, which can be incorporated in various ways and at different stages of the therapeutic process. Despite its increasing implementation, conceptual and terminological inconsistencies continue to impede a shared understanding of the competencies required for its effective delivery. Objective: This theoretical paper introduces a framework of psychotherapeutic competencies in BCP, designed to provide conceptual clarity. Methods: The framework was developed in four steps. (1) An exploratory literature search was conducted to identify existing competence and educational frameworks relevant to psychotherapy, digitalization, and educational psychology. (2) These models informed a preliminary theoretical framework. (3) This preliminary framework was critically reviewed and refined in an expert workshop with 5 licensed psychotherapists and researchers experienced in BCP. (4) Lastly, findings from a systematic review of 35 studies on BCP competencies were integrated, resulting in the final 8-domain framework. Results: The resulting framework comprises Modalities of BCP (eg, face-to-face, videoconferencing, and apps), Competence Facets (knowledge, skills, and attitudes), as well as 8 Competence Domains (Creation of Setting and Infrastructure; Transfer of Communication and Methods; Therapeutic Relationship; Self-Regulation, Self-Confidence, and Self-Care; Data Security, Legal Issues, Ethics, and Safety; Technological Aspects; Scientific Knowledge and Methods; and Individual and Cultural Diversity). Conclusions: This conceptual contribution aims to strengthen the theoretical foundation of BCP, support competence-based education for psychotherapists, and guide future research toward the empirical validation and refinement of BCP-specific competencies.

Background: Learning analytics dashboards (LAD) have been developed as feedback tools to help students self‐regulate their learning (SRL) by using the large amounts of data generated by online learning platforms. Despite extensive research on LAD design, there remains a gap in understanding how learners make sense of information visualised on LADs and how they self‐reflect using these tools.Objectives: We address this gap through an experimental study where a LAD delivered personalised SRL feedback based on interactions and progress to a treatment group, and minimal feedback based on the average scores of the lecture to a control group.Methods: After receiving feedback, students were asked to write down how they planned to adjust their study habits. These reflection texts are the target of this study. Three human coders analysed 1251 self‐reflection texts from 417 students at three different times, using a coding system that categorised learning strategies, metacognitive strategies and learning materials.Results and Conclusions: Our results show that learners who received personalised feedback intend to focus on different aspects of their learning in comparison to the learners who received minimal feedback and that the content of the LAD influences how students formulate their self‐reflection texts. Furthermore, the extent to which students incorporated suggested behavioural changes into their reflections was predicted by state measures like perceived helpfulness of the feedback. Our findings outline areas where support is needed to improve learners' sense‐making of feedback on LADs and self‐reflection.

Groups 4 Health (G4H) is a highly effective clinical intervention which, by translating social identity principles into practice, aims to improve mental health. The present study examines the impact of G4H delivered to three cohorts of first year psychology students as the adapted version G4H-S. Social Inclusion, loneliness, social isolation, test anxiety, study satisfaction, and life satisfaction were assessed immediately before the start of the first module, during the last module, and 6 weeks after completion of the final module. Results showed positive effects of program participation on self-reported loneliness, social inclusion, test anxiety, and overall life satisfaction.

The introduction of artificial intelligence (AI) in people’s lives, including in educational settings, is happening rapidly and on a massive scale. However, AI represents a complicated and abstract concept for laypeople and is, in its entirety, still quite unfamiliar to many, including students in higher education. Metaphors may facilitate the comprehension of novel or abstract concepts in terms of something already known, and help investigate implicit beliefs that have the potential to influence an individual’s actions. This study explored undergraduate students’ (n = 124) perceptions of AI and of learning with AI by analyzing metaphors collected following an established metaphor elicitation paradigm. Students’ attitudes towards AI, AI content knowledge, and usage of AI tools were assessed. The qualitative analysis of metaphors of AI yielded nine categories (e.g., brain, human, machinery, unknown), while the analysis of metaphors for learning with AI yielded seven categories (e.g., self-regulation, educator, shared learning). Overall, the anthropomorphization of AI for both foci was observed. Many conceptualized learning with AI as learning with trustworthy support, and foregrounded the perceived facilitation of learning on the basis of AI. This study highlights the importance of fostering accurate conceptualizations of AI and its role in learning, while addressing misconceptions and overly simplistic representations. Promoting a nuanced understanding of AI is essential to ensuring its effective use as a tool that enhances, rather than impedes, learning processes.

Learning in asynchronous online settings (AOSs) is challenging for university students. However, the construct of learning engagement (LE) represents a possible lever to identify and reduce challenges while learning online, especially, in AOSs. Learning analytics provides a fruitful framework to analyze students' learning processes and LE via trace data. The study, therefore, addresses the questions of whether LE can be modeled with the sub-dimensions of effort, attention, and content interest and by which trace data, derived from behavior within an AOS, these facets of LE are represented in self-reports. Participants were 764 university students attending an AOS. The results of best-subset regression analysis show that a model combining multiple indicators can account for a proportion of the variance in students' LE (highly significant R² between 0.04 and 0.13). The identified set of indicators is stable over time supporting the transferability to similar learning contexts. The results of this study can contribute to both research on learning processes in AOSs in higher education and the application of learning analytics in university teaching (e.g., modeling automated feedback).