Developing an AI-based Platform for Enhancing Critical Thinking Skills in Higher Education

Thesis Abstract

The development of critical thinking skills is increasingly recognized as essential for fostering effective decision-making and problem-solving abilities among higher education students; however, traditional pedagogical approaches often fall short in systematically cultivating these skills. This study addresses the pressing need for innovative educational interventions by designing and evaluating an artificial intelligence (AI)-based platform tailored to enhance critical thinking in undergraduate and postgraduate students. The primary aim of the research is to develop a technologically driven solution that leverages AI algorithms to personalize learning experiences, thereby fostering higher-order cognitive skills. The specific objectives include (1) to conceptualize the framework of an AI-driven platform integrating critical thinking pedagogies; (2) to implement the platform within selected higher education institutions; (3) to assess its usability, engagement levels, and effectiveness in improving critical thinking skills; and (4) to identify factors influencing students’ adoption and sustained use of the platform. The study adopts a mixed-methods research design, combining quantitative experimental and correlational analyses with qualitative thematic investigations to provide comprehensive insights into platform efficacy and user experience. The population comprises undergraduate and postgraduate students enrolled in diverse faculties at a public research university, totaling approximately 1,200 students. A stratified random sampling technique selected 300 participants for the intervention, with 150 assigned to the experimental group using the AI platform and 150 to the control group engaged in traditional learning activities. Data collection instruments include pre- and post-intervention critical thinking assessments based on the California Critical Thinking Skills Test (CCTST), user engagement surveys, system usability scales, and semi-structured interviews with a subset of 30 participants. Validity and reliability of quantitative instruments are established through pilot testing, Cronbach’s alpha coefficients exceeding 0.85, and construct validity confirmed via confirmatory factor analysis. Data analysis involves descriptive statistics, paired-sample t-tests, ANCOVA to adjust for baseline differences, and multiple regression analysis to examine predictors of critical thinking improvement. Thematic analysis of qualitative interview data offers nuanced insights into user perceptions and contextual factors affecting platform adoption. The study is guided by Bloom’s Taxonomy and Vygotsky’s Social Development Theory, adapted for digital learning environments, to inform platform architecture and pedagogical strategies. It is anticipated that the AI-based platform will significantly outperform traditional methods in enhancing students’ critical thinking scores, as indicated by statistically meaningful increases in post-test assessments. User engagement metrics are expected to correlate positively with skill improvement, while qualitative findings are projected to reveal high levels of usability, perceived relevance, and motivation among users. Such outcomes are expected to contribute new evidence on the integration of AI technologies within higher education to support cognitive skill development. The research advances the theoretical understanding of digital pedagogies by operationalizing the intersection of AI and critical thinking development; practically, it offers a scalable, adaptable platform that can be integrated into existing curricula to foster reflective and analytical capacities. The findings will inform policymakers, educators, and technologists about effective design principles and implementation strategies for AI-enabled educational interventions aimed at critical thinking enhancement. In conclusion, the study underscores the transformative potential of AI-driven platforms in revolutionizing higher education learning paradigms. Recommendations include adopting the platform across diverse disciplinary contexts, continuous refinement based on user feedback, and further longitudinal research to assess long-term impact. Future studies should explore scalability, integration with other emerging technologies such as virtual reality, and the role of adaptive learning algorithms in tailoring critical thinking exercises to individual learner profiles.