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

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The 20th International Symposium on Electrets (ISE 20) was held in Matsue, Japan, at the “Kunibiki Messe” Shimane Prefectural Convention Center from September 1 to 5, 2025 (Figs. 1 and 2). This event was sponsored by the IEEE Dielectrics and Electrical Insulation Society, and hosted by Prof. Yuji Suzuki of The University of Tokyo and Prof. Takashi Nakajima of Tokyo University of Science.

More than 100 participants from 15 countries across Asia, Europe, and North America attended to discuss and exchange the latest advances in electret science, dielectric materials, and electrostatic phenomena. By bringing together experts from academia and industry, ISE 20 provided an international platform for the exchange of research findings and innovative ideas, contributing to future advances in both scientific understanding and practical technologies. The symposium also strengthened international collaborations and promoted interdisciplinary research, reinforcing the important role of electret science in emerging technologies such as energy harvesting, flexible electronics, sensors, and biomedical applications.

The scientific sessions include the Bernhard Gross Memorial Lecture delivered by Prof. Michael Wu bbenhorst (KU Leuven), 6 plenary lectures, 47 oral presentations, and 41 poster presentations. A special tribute session was also held to honor Eiichi Fukada. Attendees also enjoyed exploring Matsue city, a historic castle town known for its beautiful lakes, traditional tea culture, and rich heritage.

Preceding ISE 20, a tutorial session with four lectures on recent topics in electrets was given by Prof. Xiaoqing Zhang (Tongji University), Prof. Masahiro Sato (The University of Tokyo), Prof. Yuji Suzuki (The University of Tokyo), and Prof. Yumi Tanaka (Tokyo University of Science). This was followed by a Special Session on the “100th Anniversary of Mototaro Eguchi’s Carnauba-Wax Electret.” In this session, to celebrate 100 years from his first article on electret in, “a short history of Mototaro Eguchi” by Prof. Yuji Suzuki (The University of Tokyo), “surface potential measurements of the Eguchi’s Electret in 2024” by Dr. Yoshinobu Yasuno (Kobayashi Institute of Physical Research), “Mototaro Eguchi and physics in early 20th-century Japan” by Dr. Hiroto Kono (National Museum of Nature and Science, Japan), and “early electret research after Eguchi’s seminal work by Prof. Reimund Gerhard (University of Potsdam) were given.

During the closing ceremony of ISE 20, the 2025 Dilip Das-Gupta Award for Best Presentation by Young Scientist was awarded to “extension of piezoelectret theory and its importance for the development of piezoelectret metamaterials,” by Qianqian Hu at Togji University, “self-powered piezo-/pyroelectric integrated devices using electrospun fluoropolymer electret fibers,” by Krittish Roy at University of Limerick, and “gait detection system using piezoelectric matrix sensor” by Yuki Sakurai at Tokyo University of Science.

In ISE 20, research presentations covered a wide range of topics, including charge injection/transport/trapping; decay mechanisms in dielectric materials; ferroelectric, piezoelectric, and pyroelectric materials; organic and polymer electronics and transducers; bioelectrets and biomaterials; nanoscale electrostatic phenomena; and advanced characterization techniques. Considerable attention was also given to emerging applications such as flexible and stretchable electronics, wearable sensors, actuators, electret/triboelectric energy harvesting devices, microelectromechanical systems (MEMSs), and sustainable energy technologies.

This Special Issue of the IEEE Transactions on Dielectrics and Electrical Insulation covers these topics with a collection of 20 carefully reviewed papers, most of which were presented at ISE 20. These contributions provide a state-of-the-art overview of electret science and applications, demonstrating its interdisciplinary reach across condensed matter physics, chemistry, materials science, electrical/mechanical engineering, and life sciences.

We extend our sincere appreciation to all the authors and reviewers for their valuable contributions and support. We also thank the Editor-in-Chief, Prof. Michael Wübbenhorst, for his pivotal role in making this Special Issue possible. Finally, we thank all the participants, the Scientific Advisory Committee of ISE, benefactors, and sponsors, whose contributions greatly enhanced the organization and scientific quality of the symposium.

Post-operative infections remain a challenge in implant surgery, leading to prolonged treatment, increased costs, and implant failure. Localized antibiotic-delivery coatings are a promising strategy to reduce infection risk while maintaining biocompatibility. Here, we evaluate cold atmospheric pressure plasma (CAP) spraying as a solvent-free method to deposit polyvinyl alcohol (PVA) layers containing amikacin on stainless steel and to identify plasma parameters that control release and antibacterial activity. A 3 × 3 factorial design varied nozzle distance (15, 20, 25 mm) and speed (10, 15, 20 cm/s). Surface morphology was assessed by optical microscopy, amikacin release quantified by HPLC-HRMS, and antibacterial activity tested against Staphylococcus aureus and Escherichia coli using Kirby–Bauer disc diffusion. Two-way ANOVA with Tukey post hoc tests and nonparametric validation were applied. Cold plasma spraying speed significantly affected drug release, whereas distance and the interaction term were not significant. Lower spraying speeds produced thicker, more porous coatings with greater cumulative release and larger inhibition zones. Drug release profiles were best described by Weibull and first-order models, showing an initial burst followed by sustained release. These findings indicate that CAP spraying enables solvent-free fabrication of antibiotic-loaded PVA coatings with tunable release, and optimizing spraying speed improves coating mass, drug delivery, and antibacterial performance.

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.

Reflection silencers are installed in the exhaust system of stationary combustion engines to attenuate low-frequency noise by means of destructive interference. The acoustic properties of mufflers are experimentally determined by the standard two-load method, which only considers measurements without mean flow. In real engine operation, however, exhaust mass flow is always present. Measurements are significantly more complex and expensive if fluid flow is taken into account, which is why the available data is limited. Thus, the impact of mean flow on the attenuation of silencers is not clearly known yet. This work contributes to the state of the art by quantifying the influence of the Mach number on the transmission loss of double-tuned straight-through mufflers based on reproducible, noise corrected measurement results that include uncertainties. A frequency range between 20 Hz and 891 Hz is investigated at eleven different Mach numbers between 0 and 0.1 under ambient conditions. It is found that resonance peaks diminish with increasing Mach number, while other frequencies remain unaffected by mean flow. These findings can be transferred to operating conditions of stationary combustion engines and other exhaust systems. The experimental data will serve as a basis for the validation of analytical and numerical models in subsequent work.

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.