This study aims to develop and validate a physics learning module based on a contextual inquiry approach assisted by a Learning Management System (LMS) that is valid, practical, and effective in improving the self-regulation skills and digital literacy of high school students. The research uses the Research and Development (R&D) method with the ADDIE model which is limited to the Implementation and Evaluation stage. The subjects of the study were 25 students of grade XI Science 2 SMAN 1 Dompu on Hydrostatic Pressure material with LMS Teacher's Room and PhET simulation. The results of validation by three experts showed an average percentage of 87.50% (Very Valid category). The practicality of the module was shown from the students' response of 88.10% and the teacher's response of 85.00%, both in the Very Practical category. The self-regulation questionnaire reached 90.00% and digital literacy reached 95.00%, both in the Very Good category. The effectiveness test using Normalized Gain (N-Gain) yielded an average of 0.79 (High category), with a minimum score of 0.36 and a maximum of 0.98. The modules developed have proven to be valid, practical, and effective as innovative technology-based teaching materials that are able to facilitate students' self-regulation and digital literacy skills.
The integration of Generative Artificial Intelligence (GenAI) has transformed higher education, offering innovative possibilities as well as pedagogical challenges. This study examines students' perceptions and expectations regarding the use of AI as a learning assistant in Fundamental Physics Course. Employing a quantitative descriptive survey design based on the Technology Acceptance Model (TAM) and self-regulated learning frameworks, data were collected from 119 undergraduate students in Physics and Mathematics Education at University of Mataram using a 5-point Likert scale questionnaire. The findings indicate highly positive perceptions across all evaluated dimensions: Perceived Ease of Use ( ), Student Attitudes ( ), Perceived Usefulness ( ), Usage Intensity ( ), and Learning Impact ( ). Students predominantly utilize AI as a cognitive scaffold to summarize lecture materials ( ) and facilitate self-directed learning outside class hours ( ). Within the Conceptual Change Model (CCM-CCA), AI effectively supports abstract concept understanding ( ) and connects theory with real-world applications ( ). Furthermore, students demonstrated strong academic maturity, recognizing that AI serves as a tool rather than a replacement for lecturers ( ) and acknowledging ethical guidelines ( ). However, the relatively lower score on critical thinking development ( ) highlights the risk of cognitive offloading and misconceptional pitfalls stemming from AI hallucinations. This study underscores the need to enhance AI literacy and redesign physics assessments toward higher-order thinking skills to ensure deep conceptual mastery.
This study aims to examine the effect of the guided inquiry learning model assisted by PhET simulation on students’ learning outcomes in static fluid. The study employed a quasi-experimental method using a nonequivalent control group design. The population consisted of 93 Grade XI students at SMAN 1 Kediri, from which 41 students were selected through purposive sampling. Class XI-A1, comprising 21 students, served as the experimental group and received guided inquiry instruction assisted by PhET simulation, while Class XI-A2, comprising 20 students, served as the control group and received conventional instruction. Cognitive learning outcomes were measured using an 18-item multiple-choice test that had been validated in terms of validity, reliability, difficulty level, and discriminating power. Observation sheets were also used to assess students’ affective and psychomotor outcomes as supporting data. Data analysis included normality and homogeneity tests, followed by an independent-samples t-test using the pooled-variance formula at a 5% significance level. An N-gain analysis was also conducted to assess the improvement in cognitive learning outcomes. The results showed that the calculated t-value (t = 3.46) exceeded the critical value (t = 2.02), indicating a statistically significant difference between the experimental and control groups. The N-gain results placed the experimental group in the high category and the control group in the low category. The mean affective scores of the experimental and control groups were 84.92 and 82.24, respectively, both of which were categorized as good. The experimental group also obtained a higher mean psychomotor score than the control group (85.71 vs. 80.58). These findings indicate that the guided inquiry learning model assisted by PhET simulation has a significant positive effect on students’ learning outcomes in static fluid.
This study aims to examine the effect of the Problem-Based Learning (PBL) model with a multimodal approach on students’ physics creativity. This study used a quasi-experimental method with a nonequivalent control group design. The study was conducted at SMAN 3 Praya in class XI of the even semester of the 2025/2026 academic year on the topic of static fluid. Sampling used a purposive sampling technique, selecting class XI F1 with 26 students as the experimental group and class XI F2 with 28 students as the control group. The research instrument was a creativity test consisting of 4 essay questions covering the indicators of fluency, flexibility, originality, and elaboration. Data analysis was performed using the independent sample t-test with the pooled variance formula. The results showed that the PBL model with a multimodal approach significantly influenced students’ creativity, as indicated by a of 2.816 greater than of 2.007 at a 5% significance level. The average posttest score of the experimental class reached 74.81, higher than the control class at 63.75, with an N-Gain of 0.652 (moderate category) for the experimental class compared to 0.474 (moderate category) for the control class. Thus, the PBL model with a multimodal approach is effective in improving students’ physics creativity.
The integration of Artificial Intelligence (AI) into educational contexts has significantly reshaped the landscape of teaching and learning, particularly within Science, Technology, Engineering, and Mathematics (STEM) education. Physics, as a discipline characterized by abstract concepts and complex problem-solving processes, often presents challenges for learners when approached through conventional instructional methods. In response, AI-supported virtual STEM learning environments have emerged as a promising alternative, offering interactive, adaptive, and personalized learning experiences. This study aims to explore how AI-supported virtual STEM learning contributes to the development of twenty-first century skills, including critical thinking, creativity, collaboration, and digital literacy. Employing a literature review approach guided by the PRISMA framework, this study systematically identifies, screens, and analyzes relevant scholarly articles published between 2018 and 2024. The analysis focuses on identifying patterns in instructional design, types of AI technologies used, and their pedagogical impact on physics learning. The findings indicate that AI-driven tools—such as intelligent tutoring systems, adaptive simulations, and virtual laboratories—can enhance student engagement and foster higher-order thinking skills. These technologies enable learners to actively construct knowledge, experiment in safe virtual environments, and receive immediate feedback tailored to their learning needs. However, the review also reveals that the number of empirical studies in this area remains limited, indicating that the field is still in a developmental stage. This study contributes to the growing body of knowledge by synthesizing current research and highlighting both the opportunities and limitations of AI integration in physics education. It also underscores the need for more comprehensive and methodologically rigorous studies to strengthen the evidence base.
