What do students know about self-regulated learning with GenAI? A situational judgment test

Authors: Nick Naujoks-Schober & Marion Händel

Abstract

The rapid diffusion of generative artificial intelligence (GenAI) tools such as ChatGPT is reshaping students’ self‑regulated learning (SRL) practices, yet little is known about learners’ conditional knowledge for strategically employing these technologies. To address this gap, we developed and preliminarily validated a situational judgment test that measures students’ strategic knowledge of interacting with GenAI across two typical higher‑education learning scenarios. Following a multi‑step design, we (1) conducted a literature review to identify the most frequent cognitive, metacognitive, and resource‑oriented learning situations involving GenAI; (2) performed problem‑centered interviews with 18 students from eight disciplines to elicit real‑world strategies; and (3) refined these strategies into 36 items spanning the forethought, performance, and self‑reflection phases of SRL for two generic scenarios—planning a study project and creating a summary. Experts (N = 48 SRL scholars and AI specialists) rated the usefulness of each strategy on a six‑point Likert scale, yielding significant pairwise discriminations for the majority of items (28/36 for the summary scenario, 21/36 for the planning scenario). The situational judgment test thus captures conditional knowledge of SRL‑GenAI interaction, offering a robust instrument for assessing metacognitive competence beyond self‑report AI‑literacy measures. Findings will inform educators on students’ knowledge about SRL with GenAI and may guide interventions to foster effective, reflective AI‑enhanced learning.

1.      Introduction and aims

According to models of self-regulated learning (SRL), students should actively manage their learning processes using situation-specific learning strategies (Panadero, 2017). To employ the most effective strategy in a given context, students require conditional knowledge regarding their strategy use. To assess this knowledge, situational judgment tests compare students’ evaluations of strategy usefulness in realistic, hypothetical learning scenarios against expert benchmarks (Dörrenbächer-Ulrich et al., 2024; Pfost & Hübner, 2025).

However, the rapid rise of generative artificial intelligence (GenAI) applications, such as ChatGPT, is transforming learning scenarios and strategies associated with student SRL (Chiu, 2024). Current research discusses potential benefits of integrating GenAI into higher education for individualizing learning, for instance, through quizzes generated with students’ lecture notes (Dillon, 2024; Gärtner et al., 2024). Conversely, first studies highlight challenges in SRL with GenAI as students may delegate tasks too extensively to the AI (cognitive offloading; Lee et al., 2025) or insufficiently monitor their own learning and interaction with the AI (metacognitive laziness; Chardonnens, 2025; Fan et al., 2025).

According to the current state of research it is unclear whether students possess the strategic knowledge necessary to successfully interact with GenAI during self-regulated learning. Previous measurement instruments to assess AI literacy have focused either on students' self-reports (Almatrafi et al., 2024) or on their knowledge about GenAI specifications (Markus et al., 2025). We therefore aimed to develop and validate a situational judgment test to assess students’ knowledge of how to strategically interact with GenAI during their self-regulated learning.

2.      Method

To develop a situational judgment test, we followed a multi-step procedure: (1) We performed a literature search to identify scenarios of leaning with GenAI, (2) we conducted problem-centered interviews with students to select relevant scenarios and to identify strategies to interact with GenAI across different disciplines, and (3) we obtained expert ratings on the consequently developed situational judgment test.

Based on 60 publications, we extracted the three most frequent cognitive, metacognitive, and resource-oriented scenarios. Those scenarios were used for problem-centered interviews with students to analyze their strategy use during GenAI interactions. Using a maximum-variation sampling approach, interviews were held with 18 students across eight different study programs. Students were interviewed regarding their learning behavior when interacting with GenAI. Drawing on the Self-Regulated Learning Interview Schedule (Zimmerman & Pons, 1986), participants first rated the relevance of nine given scenarios (e.g., using GenAI to create a summary) regarding their own learning. Next, students described their strategic approach for those scenarios that they had rated as relevant. Both the content and usefulness of the reported strategies were coded using qualitative content analysis (Mayring & Fenzl, 2019), employing both inductive category development and deductive assignment to overarching learning strategy categories.

In the next step, we extracted strategies for the most relevant scenarios from the interviews. These strategies were iteratively refined according to low, middle, and high usefulness based on the degree of students’ (meta-)cognitive activity and the suitability of the GenAI. This final test was given to experts from the fields of self-regulated learning (N = 31) and/or artificial intelligence (N = 17) who rated the usefulness of the strategies on a 5-point Likert scale from 1 (very low) to 5 (very high).

3.      Results

Identification of scenarios. From the literature review, both discipline-specific and cross-disciplinary scenarios and strategies were identified. Especially the generic scenarios provided a solid foundation for the development of the situational judgment test, which is intended to be applicable to students across all fields.

Development of the situational judgment test. Two of the most relevant and generic scenarios for learning with GenAI were selected for the test, namely planning a study project and creating a summary. For each scenario, strategy options were identified from the interviews that differed in terms of their quality and usefulness for learning purposes. In line with Zimmerman's model (1986), each scenario was structured along the three phases of self-regulated learning. That is, the forethought phase before planning (p)/summarizing (s), the performance phase during p/s, and the self-reflection phase after p/s. For each scenario and each phase, six strategies were developed, resulting in 36 strategies differing in usefulness.

Usefulness of strategies (expert ratings). Initial descriptive observations of the expert ratings and two-factor variance analyses for Friedman ranks in connected samples indicate theoretically valid and significant pair comparisons of the strategies. Across all phases of the summary scenario, the expert ratings confirmed 28 out of 36 pair comparisons. In the planning scenario, however, the expert judgments showed only 21 valid pair comparisons. At the time of the conference, data from a student sample from different disciplines will be available, which allows for an initial validation of the test instrument.

4.      Theoretical and educational significance of the research

This research addresses the rapidly changing learning behavior shaped by GenAI, where SRL competencies are becoming increasingly vital for actively engaging in and maintaining an overview of one's own learning process. Additionally, by using typical scenarios in higher education and comparing student and expert ratings, the developed test goes beyond current self-reports of AI literacy and knowledge tests about GenAI. In doing so, the situational judgment test also captures an aspect of higher metacognitive skills through conditional knowledge of strategy use, which is often overlooked in other operationalizations of AI literacy (Almatrafi et al., 2024). As metacognitive monitoring and learners’ cognitive engagement seems central for self-regulated learning with GenAI interaction, the situational judgment test should provide a robust measure even against the rapid development of GenAI models.

Assessing conditional knowledge for strategic learning with GenAI also provides educators with an estimate of the learners’ existing competence in this regard. Based on students' answers, it will also be possible to identify how students consider GenAI useful across different learning scenarios. These insights reveal, for example, whether pure task outsourcing is considered more useful than targeted co-constructive processes, and whether students recognize their own (meta-)cognitive activity as a central element. Future research will focus on how students can be supported in their interactive process with GenAI.

5.      References

Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6, 100173. https://doi.org/10.1016/j.caeo.2024.100173

Chardonnens, S. (2025). Adapting educational practices for Generation Z: Integrating metacognitive strategies and artificial intelligence. Frontiers in Education, 10, Article 1504726. https://doi.org/10.3389/feduc.2025.1504726

Chiu, T. K. F. (2024). A classification tool to foster self-regulated learning with generative artificial intelligence by applying self-determination theory: A case of ChatGPT. Educational Technology Research and Development, 72(4), 2401–2416. https://doi.org/10.1007/s11423-024-10366-w

Dillon, T. (2024). Korean university students’ prompt literacy training with ChatGPT: Investigating language learning strategies. English Teaching, 79(3), 123–157. https://doi.org/10.15858/engtea.79.3.202409.123

Dörrenbächer-Ulrich, L., Sparfeldt, J. R., & Perels, F. (2024). Knowing how to learn: Development and validation of the strategy knowledge test for self-regulated learning (SKT-SRL) for college students. Metacognition and Learning, 19(2), 1–45. https://doi.org/10.1007/s11409-024-09379-w

Fan, Y., Tang, L., Le, H., Shen, K., Tan, S., Zhao, Y., Shen, Y., Li, X., & Gašević, D. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. https://doi.org/10.1111/bjet.13544

Gärtner, C., Moraß, A., Koss, S., Garbusa, S., Matern, S., Innermann, I., & Forschungs- Und Innovationslabor Digitale Lehre, F. (2024). Einsatz, Nutzen und Grenzen von ChatGPT und anderen Large Language Modellen an den bayerischen HAWs (No. 5; Die Studien- und Schriftenreihe des Forschungs- und Innovationslabors Digitale Lehre – FIDL). FIDL – Forschungs- und Innovationslabor Digitale Lehre. https://doi.org/10.34646/THN/OHMDOK-1466

Lee, H.-P. (Hank), Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R., & Wilson, N. (2025). The impact of generative AI on critical thinking: Self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Article 1121. https://doi.org/10.1145/3706598.3713778

Markus, A., Carolus, A., & Wienrich, C. (2025). Objective measurement of AI literacy: Development and validation of the AI competency objective scale (AICOS). Computers and Education: Artificial Intelligence, 9, 100485. https://doi.org/10.1016/j.caeai.2025.100485

Mayring, P., & Fenzl, T. (2019). Qualitative Inhaltsanalyse. In N. Baur & J. Blasius (Eds.), Handbuch Methoden der empirischen Sozialforschung (pp. 633–648). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-21308-4_42

Panadero, E. (2017). A review of self-regulated learning: Six models and our directions for research. Frontiers in Psychology, 8, Article 422. https://doi.org/10.3389/fpsyg.2017.00422

Pfost, M., & Hübner, V. (2025). Assessment of strategic knowledge of learning from errors in higher education. Diagnostica, 71(2), 53–63. https://doi.org/10.1026/0012-1924/a000341

Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key subprocesses? Contemporary Educational Psychology, 11(4), 307–313. https://doi.org/10.1016/0361-476x(86)90027-5

Zimmerman, B. J., & Pons, M. M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal, 23(4), 614–628. https://doi.org/10.3102/00028312023004614

Social Media spielt eine zentrale Rolle in der Unternehmenskommunikation und erfordert ein systematisches Verständnis von Plattformdynamiken, datengestützten Entscheidungen und neuen Technologien. Zudem verändert der Einsatz von Künstlicher Intelligenz Themen wie Content-Erstellung, Zielgruppenansprache und Kampagnenoptimierung und wirft zugleich Fragen hinsichtlich deren strategischer Nutzung auf. Der vorliegende Beitrag stellt die Ergebnisse einer Studie vor, die aufzeigt, wie Social-Media-Verantwortliche die Entwicklungen im Arbeitsalltag wahrnehmen und gestalten. Im Fokus stehen dabei Plattformwahl, KI-Einsatz und strategische Priorisierung. Die dafür zugrunde liegende Online-Befragung wurde unter Social-Media Verantwortlichen von April bis Juni 2025 durchgeführt. Die Ergebnisse zeigen, dass durchschnittlich 4,5 Plattformen parallel betreut wer den, wobei Instagram, Facebook und LinkedIn dominieren. Zeitgleich erhöhen neu aufkommende Plattformen und Formate zunehmend die Komplexität. KI-Tools wer den vorwiegend für operative Aufgaben genutzt, wie Content-Erstellung, Social Listening und Kundenservice. Strategische Anwendungen sind unterrepräsentiert und auch das Potenzial für die operative Nutzung wird noch nicht optimal aus geschöpft. Zeitgleich zeigen sich klare Defizite in der strategischen Verankerung: Lediglich 43% der Befragten sind mit ihrer Social-Media-Strategie zufrieden und die Erfolgsmessung wird vor allem über operative Kennzahlen durchgeführt, wobei der wirtschaftliche Beitrag dabei häufig unklar bleibt. Die Studie verdeutlicht damit ein klares Spannungsfeld zwischen steigender Komplexität, zunehmendem Technikeinsatz und unzureichender strategischer Integration. Daraus werden schließlich zentrale Handlungsfelder für ein effektives Social-Media Management abgeleitet.

This position paper investigates the effects of media induced expectations and perceived control on the accep-tance of a social robot in a 2x2 between-subjects laboratory pre-study that simulates a medical history interview in the emer-gency room of a hospital. 52 participants are presented with a priming media report beforehand, conveying either a positive or negative view of robotics and artificial intelligence in the con-text of data privacy to induce either positive or negative expec-tations. Subsequently, the participants interact with the avatar of a social robot either with or without a choice of sharing their own sensitive data. Acceptance was then assessed using a post-reception questionnaire. Results indicate a significant increase of perceived safety by positive priming on the one hand, and - in combination with providing cognitive control in view of sensitive data protection - a significant raise of trust in and subjective performance assessment of the robot, on the other hand.

In this paper, we analyze data from a study [10] on cognitive load dependency of anthropomorphism towards exploring the strength of the association of negative attitudes towards robots and anthropomorphism under cognitive load induction by a secondary task. A cooperative pick-and-place study with a non-social robot arm was conducted as main task and number memorization as secondary task inducing extraneous cognitive load. Even though ANCOVA results are insignificant, correlations show an interesting trend of weaker association of anthropomorphism and negative attitudes towards emotions in interaction with robots under high cognitive load. So, the positive association of cognitive load and anthropomorphism to some extent seems to compensate the negative association of negative attitudes and anthropomorphism, which may be explained by lack of inhibition of the default social cognitive pathway in the human brain due to depletion of cognitive resources. Thus, both parameters should be considered in robot design, especially in applications, where the impact of anthropomorphism on collaboration may pose performance or safety issues, e.g. close interaction, and further research is suggested.

Empirische Rechtswissenschaft im Fokus: Dieses Buch bietet einen fundierten Überblick über Stand und Perspektiven interdisziplinärer Rechtswissenschaften. Von Verfassungs-, Europa-, Kartell- und Strafrecht über richterliches Entscheidungsverhalten bis hin zu Fragen von Politikberatung, Arbeitsmärkten und Gemeinschaftsgütern entfalten die Beiträge ein breites thematisches Spektrum. Zugleich zeigen sie, wie Verhaltenswissenschaften, datengetriebene Methoden und Künstliche Intelligenz unser Verständnis des Rechts vertiefen und verändern. Sie zeichnen dabei auch das Wirken von Christoph Engel, Direktor am Max-Planck-Institut zur Erforschung von Gemeinschaftsgütern, nach. 

This paper explores whether cognitive load affects intervention behavior of a driver in terms of taking control of the driving situation in autonomous driving. A video of a simulated autonomous driving scenario from a driver's perspective including critical situations is presented to the participants. In parallel, extraneous cognitive load is induced by letting participants mentally solve arithmetic problems as a secondary task. Whenever participants feel that they want to intervene and take control of the driving situation, they are required to press the button of a stopwatch. In contrast to expectations of increased impulsive behavior, results reveal an inverse association of cognitive load and counts of interventions paired with increased trust in the autonomous vehicle, but reduced perceived safety and situation awareness. This behavior may be explained with impaired switching of attentional focus and lack of possibility of internalization of the primary task under depletion of cognitive resources. The observed effects are suggested to be accounted for towards shared autonomy concepts in order to promote the design of safe autonomous vehicles and their integration in society.

In today’s fast-paced digital environment, it is essential for marketing professionals to allocate advertising budgets efficiently and maximize return on investment. By leveraging machine learning, particularly regression trees, key success metrics such as cost per click in online advertising campaigns can be predicted and optimized, enabling more effective budgeting decisions in marketing performance optimization. Showcasing a practical use case with a synthetic data set, this study outlines the relevant phases in applying ML to campaign optimization and demonstrates that the advantages of regression trees lie in their transparency and interpretability. Compared to more complex AI models, the results of regression trees are easier for managers to understand and translate into concrete actions. For instance, the models help managers to understand ad effectiveness of specific ad design and campaign decisions before they run costly live tests on ad platforms. Furthermore, the models allow for lean analysis of ad effectiveness across platforms. Finally, they allow capturing nonlinear effects of ad design and campaign decisions on ad effectiveness, thus avoiding overly simplistic analysis results. Therefore, this study shows the application of regression trees, as step-by-step guide for managers and actionable implications, especially for small- and medium sized enterprises adopting AI in marketing.

Human Motion Simulation (HMS) predicts and visualizes how virtual agents move and behave in various scenarios, aiding decision-making. However, it does not communicate the experience behind agents' behavior. To address this, we present Embodied Spatial Simulation (ESS), a concept combining augmented reality(AR) and HMS to support in-situ decision-making. It enables an XR user to create “what-if” scenarios, simulate virtual agents' behavior in situ, and experience the scenarios from their perspectives through embodiment. To demonstrate ESS, we developed a proof-of-concept prototype featuring two scenarios (e.g., fire evacuation planning). From expert walkthroughs (N=4) and a survey (N=38) with different professions, we found that incorporating embodiment into simulations can change users' intentions to use simulation outcomes. Rather than directly obtaining solutions from the results, users showed more interest in understanding the experience behind simulated behavior and using this insight to shape decisions. We discuss the potential of ESS to support future human-centered design practices.

What characterizes individuals who stand up and take action against violations of fundamental moral principles, even in the face of personal risk and adversity? Due to methodological and ethical challenges, we are limited in our understanding of personality dispositions that predict who acts morally courageously in situations of severe wrongdoings and considerable risk. In Germany and Austria, we recruited recipients of public awards for outstanding moral courage (n = 54) and individuals who nominated themselves to have acted morally courageously (n = 48). We contrasted these morally courageous individuals with a demographically matched reference group of people who reported not to have acted morally courageously before (n = 323), and with normative samples of the German population. Results showed three key patterns: First, among the HEXACO personality factors, heightened extraversion consistently distinguished the morally courageous groups from the reference group. Second, membership in the morally courageous groups was predicted by heightened moral attentiveness and anger proneness, and lowered endorsement of loyalty and authority, indicating exceptional moral functioning of the morally courageous. Third, the morally courageous were characterized by diminished risk avoidance and social anxiety, suggesting that they encounter a lower psychological barrier posed by fear of negative social evaluation.

Künstliche Intelligenz (KI) ist unverzichtbar im modernen Marketing und dient zunehmend als strategischer Wettbewerbsvorteil in innovationsgetriebenen Branchen. Das Interesse an generativer KI wächst auch in der Marketinginnovation. Marketer nutzen zunehmend Sprachmodelle (Large Language Models, LLMs) wie ChatGPT, um Kreativität, Geschwindigkeit und Effizienz zu steigern. Die Forschung zur Schnittstelle von generativer KI und Marketing ist bislang fragmentiert und meist auf spezifische Anwendungsfälle fokussiert. Es fehlt ein ganzheitlicher Ansatz, der den Einsatz generativer KI über den gesamten Marketinginnovationsprozess systematisch analysiert. In einem systematischem Literaturüberblick zeigt dieser Beitrag Potenziale und Herausforderungen auf und demonstriert, dass KI in allen Innovationsphasen einsetzbar ist und dabei drei Rolle einnehmen kann: als Unterstützer, als Erweiterung oder als eigenständiger Akteur, mit entscheidendem Einfluss auf Innovationsprozesse.

Human–Robot Interaction (HRI) education has largely emphasized technical skills, while psychological and socio-cognitive dimensions of interaction remain underrepresented. Yet contemporary HRI research increasingly views robots as social actors that shape perception, behavior, and decision-making. To address this gap, this paper introduces the Scientific HRI Didactic Framework (SHRI-DiF), a didactical approach that integrates research-based learning with the 5E cycle (Engage–Explore–Explain–Elaborate–Evaluate). The framework positions students as active researchers who conduct the full empirical process, from identifying research questions to evaluating findings. Social robots serve as epistemic tools and experimental agents to investigate social and psychological constructs such as trust, perceived control, and acceptance. The framework is demonstrated in a 12-week undergraduate seminar in business and media psychology using the Furhat robot for dialog strategies in HRI. Results indicate that authentic HRI research settings foster scientific reasoning, methodological competence, and interdisciplinary thinking, offering a transferable model for research-based higher education.

As generative Artificial Intelligence (AI) becomes increasingly embedded and utilized for digital design, it presents both opportunities and risks. One major concern is its potential to facilitate and incorporate deceptive design patterns into computing technologies, which could manipulate or mislead users to their disadvantage. Similar to the concept of precedent-based design, a common approach in design theory that suggests reapplying previous design solutions to similar or identical problems, generative AI can integrate deceptive design patterns included in the training data a model has seen before. Our workshop explores how generative AI suggests and enacts deceptive design patterns in digital design. The goal of the workshop is to explore the ethical challenges of utilizing generative AI models and develop strategies to detect or prevent manipulative practices, thereby creating more transparent and equitable AI-generated experiences.