Social constructivism theory embraces peer-to-peer communication that helps students understand, examine, and discern the process of knowledge construction. The Social Constructivism Flipped-Classroom Model (SCFCM) inverts the traditional classroom paradigm by providing content outside of class, often through online materials, and devoting in-class time to active learning and discussion. This study aims to investigate the impact of the SCFCM on the conceptual understanding of photocatalysis, a crucial process in environmental science and chemistry, particularly in relation to sustainability and sustainable development. Photocatalysis, being a self-sustained process, holds potential for addressing global challenges such as renewable energy and pollution reduction, both of which are central to achieving sustainable development goals. A quasi-experimental pre-test–post-test design was employed at a public sector university, involving forty-three (43) students in each of the flipped- and non-flipped-classroom groups. Assessment tools, including pre- and post-tests and an interest survey, were used to gauge students’ conceptual understanding of photocatalysis and their degree of learning interest. The same chemistry teacher, one who had eight years of teaching experience, taught both groups. The analysis of covariance (ANCOVA) results comparing students’ performance showed a significant difference in the performance of students in the experimental group compared to the control group. The multivariate analysis of variance (MANOVA) results, however, revealed substantial differences in attention, relevance, confidence, and satisfaction between the experimental and control groups. The findings highlight that the SCFCM improved students’ understanding of complex photocatalysis concepts and demonstrated its relevance to sustainable development, offering valuable insights into the potential of this teaching approach for Science, Technology, Engineering, Mathematics (STEM) education, especially in addressing sustainability challenges.

The current study investigates the effects of a digital growth mindset on the motivation and success of chemistry students. The approach involves the use of technological tools that encourage students to face challenges and keep trying even when things become difficult. Students can achieve milestones by following this fruitful methodology. This study utilized a mixed-method design of an ordered–explanatory type, as identified in one of the categories of mixed-method approaches. The quantitative aspects of the research project were conducted using a matching-only pre-test–post-test control-group design. This was conducted because the study was carried out on secondary school students in Lahore, Pakistan, and the population included students up to the tenth grade. Only the experimental group participated in digital growth mindset activities. The control group was taught using traditional methods. The qualitative aspect of the study involved conducting focus group discussions with students in the experimental group. The results showed a significant improvement in motivation and chemistry achievement among the students in the experimental group, as evidenced by the higher mean scores from the pre-tests and the post-tests compared to the control group. The present research findings reveal that digital growth mindset interventions, when appropriately incorporated into chemistry curricula, possess the capacity to not only improve student engagement and subsequent performance but also to provide educators with valuable insights into instructional practices that are worth implementing in the digital era.

In view of the present state of climate change, it is a necessity that students learn about its impact, especially in the South Asia. The purpose is to explore the opportunity of AI pedagogy in enhancing climate literacy in university students in Pakistan, and its impact in terms of digital inclusion and student engagement as mediating variables. A theoretical model is developed in this study, grounded in Constructivist Learning Theory, Digital Equity Theory, and Self-Determination Theory. A self-developed and validated questionnaire was used to gather data from 400 students in public sector universities. PLS-SEM was used to test the model. The results especially with regard to the very high correlation coefficient between AI pedagogy and climate literacy, extend existing evidence to science. The results also show that AI pedagogy in higher learning institutions closes the educational divide and promotes awareness about climate change. Furthermore, this research offers policymakers and educators a road map for bridging the digital gap and promoting environmentally conscious education via the use of AI-Powered Pedagogical technological tools in the classroom.

The research method used was a quantitative cross sectional survey with the aim of analyzing teachers’ perceptions of the vocational rehabilitation s

AI systems are capable of carrying out activities like solving problems, interpreting language, identifying patterns, and making forecasts—typically with minimal or no direct human input. The use of artificial intelligence (AI) by both teachers and students is steadily increasing in universities; however, it remains relatively underexplored in the context of Pakistan. This study explored how familiar university students in Pakistan are with artificial intelligence tools. It also examined how often they use these tools in education and whether their attitudes and usage differ based on gender. A mixed method approach is used for this study. A survey was conducted as part of the quantitative phase, targeting students from public sector universities in District Multan, Pakistan. Using a random sampling technique, four public universities were selected, and data was collected from 930 students through multi-stage cluster sampling. For the qualitative phase, the focus was on students from Bahauddin Zakariya University, where fifteen in-depth interviews were conducted.

In this study, MumtahinGPT is presented, a Generative AI powered tool, empowering students and researchers with intelligent, personalized external examination practice. The preparatory tool is developed using the Retrieval Augmented Generation (RAG) workflow. The tool is designed with the aim to boost confidence and reduce presentation anxiety. Similarly, it improves academic performance while delivering thesis defenses, synopsis and proposal defenses. To cope this AI integration helps MumtahinGPT to retrieve relevant information from student’s uploaded documents. It augments with external knowledge in order to generate personalized questions to assist in preparation. The tool also offers feedback on user responses so as to help students to refine their answers. The tool is built with Python utilizing Gradio as User Interface (UI) and a gemini-2.5-flash LLM powered by Google API. The functionality of tools includes uploading documents, chunks creation, questions generation, lifeline features feedback and final performance report generation. The results validate the effectiveness of the tool in reducing anxiety and uplifting the student’s confidence. Further, it also assists external examiners and high ups in asking insightful questions during the defenses and presentations. The study concludes with future enhancements and broader applications in educational scenarios.