Isaac Davor

This study examined the direct and indirect relationships between interactive Ethno-STEM learning environments and 21st-century skills, and the mediating effect of student engagement. A quantitative cross-sectional survey design was used to collect data from 349 undergraduate mathematics students, and the data were analyzed using principal component analysis (PCA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). Results showed that Interactive Ethno-STEM learning environments significantly predicted critical thinking and digital literacy skills. Problem-solving skills, creativity and innovation were not directly predicted by Interactive Ethno-STEM learning environments. Student engagement partially mediated the relationship between interactive Ethno-STEM learning environments and critical thinking, fully mediated the relationships between interactive Ethno-STEM learning environments and problem-solving skills and between interactive Ethno-STEM learning environments and creativity and innovation, and did not mediate the relationship between interactive Ethno-STEM learning environments and digital literacy. The findings highlight student engagement as an important explanatory mechanism linking interactive Ethno-STEM learning with critical thinking, problem-solving skills, and creativity and innovation, whereas the relationship with digital literacy appears to operate independently of student engagement. The study extends the Ethno-STEM literature by providing evidence that student engagement is an important mechanism by which interactive Ethno-STEM learning contributes to fostering critical thinking, problem-solving skills, and creativity and innovation.

This study investigates the factors associated with university students’ intention to use Generative Artificial Intelligence (GenAI) in Ghana, with particular attention to the role of psychological barriers in shaping technology use intention. This study extends the UTAUT model by incorporating the concept of digital learning anxiety in understanding the psychological dimension of students using generative AI. The study employed a quantitative approach using a cross-sectional survey design, with data analyzed through covariance-based structural equation modeling (CB-SEM). The sample consisted of 350 university students in Ghana. Structural equation modeling and a bias-corrected percentile bootstrapping method were conducted in AMOS (v 23) to estimate the path coefficients. The results showed that performance expectancy and effort expectancy were positively associated with students’ intention to use GenAI. Additionally, a significant but small indirect association was found between effort expectancy and behavioral intention through digital learning anxiety. However, the findings indicate the insignificant influence of social influence. This study contributes to the literature by providing context-specific evidence from Ghanaian higher education on the role of Digital Learning Anxiety in students’ intention to use GenAI. However, because the study used cross-sectional data, this indirect relationship should be interpreted as an association rather than evidence of a causal mechanism. The findings suggest that students’ intention to use GenAI is associated with both their perceptions of the technology and their psychological responses to its use. These findings provide context-specific evidence on GenAI acceptance among the students surveyed.

Artificial intelligence (AI) is increasingly being integrated into higher education; however, most instructional applications emphasizeanswer generation rather than mathematical reasoning and critical verification of AI-generated mathematical solutions. The current study examined the effectiveness of the Guided AI Integration Model (GAIIM), a verification-oriented instructional framework that utilizes AI for verification of students’ algebraic problem-solving within a problem-solving context. An explanatory sequential mixed-methods design was employed. Quantitative data were collected from 86 undergraduate mathematics education students using a randomized pretest-posttest control group classroom design. During the 12-week intervention, students in the experimental group engaged in GAIIM-based instruction involving independent problem-solving, AI-generated solution comparison, error detection and justification, error resolution, and reflective verification, while the control group received conventional algebra instruction. Qualitative data were subsequently collected from 10 purposively selected students through semi-structured interviews and analyzed thematically to explain and elaborate on the quantitative findings. Students in the GAIIM group demonstrated significantly higher posttest performance in algebraic problem-solving, error detection, and error resolution after controlling for pretest scores. Qualitative findings further revealed stepwise verification, iterative engagement with AI feedback, conditional trust in AI-generated responses, and difficulties interpreting and correcting AI-generated mathematical reasoning. The findings suggest that structured AI-supported verification can support students’ algebraic problem-solving and error-verification performance.

This study investigates the mediating role of competence motivation in the relationship between exposure to the history of mathematical fallibility and students’ interest in mathematics. A cross-sectional survey design was employed using a quantitative approach. The sample comprised 285 undergraduate students of mathematics education in Ghana. Structural equation modeling and bias-corrected percentile bootstrapping were employed, and the analysis was conducted in AMOS (version 23). The results indicate that historical fallibility has a positive and significant effect on students’ competence, motivation, and interest in mathematics. Competence motivation partially mediated the relationship between the variables. The current study contributes to the existing body of knowledge by filling a gap in the literature. Previous studies have established the relationship between history and interest, but this study isolated fallibility as a motivational variable. The results also indicate that educators should draw on historical accounts of struggles to validate students' errors and foster interest. The findings suggest that integrating narratives of mathematical struggle and historical fallibility into mathematics instruction may contribute to more sustainable forms of mathematics education by promoting long-term student motivation, emotional resilience, and continued engagement with mathematics. The study further implies that humanizing mathematics through historical perspectives can support inclusive and psychologically supportive learning environments within digitally transforming higher education systems.

This study examined the association between computational thinking and mathematical problem-solving and investigated the mediating roles of perceived cognitive activation and metacognitive monitoring in technology-enhanced mathematics classrooms. A sequential explanatory mixed-methods approach was used. Using a stratified random sampling, 349 mathematics students were selected from a population of 2,400 undergraduate mathematics students in Ghana. For the qualitative phase of the study, eight of the participants from the quantitative study were selected using purposeful sampling. The qualitative data were collected using semi-structured interviews with the eight selected participants. The quantitative data were analyzed using Structural Equation Modeling (SEM) in Analysis of Moment Structures (AMOS) and thematic analysis for qualitative data. The results of the study indicated a positive and significant relationship between computational thinking and mathematical problem-solving. Metacognitive monitoring significantly mediated the relationship between computational thinking and mathematical problem-solving, whereas perceived cognitive activation did not. The qualitative results of the study indicated that the students in the study continuously monitored, evaluated, and revised their own thinking while solving computational mathematical problems. In addition, technology-rich environments supported analytical thinking but also introduced computational and conceptual challenges during mathematical problem-solving. The study concludes that computational thinking is associated with mathematical problem-solving achievement both directly and indirectly through metacognitive monitoring, whereas perceived cognitive activation does not provide a significant indirect pathway. The findings highlight the importance of integrating metacognitive scaffolding into technology-enhanced mathematics instruction to strengthen students’ reflective reasoning and problem-solving abilities.

The growing adoption of AI in higher learning institutions poses important questions regarding its effects on higher-order cognitive competencies of students. Although previous studies have investigated perceptions of learning influenced by AI, very little information exists regarding the cognitive processes involved in AI interactions influencing learning performance. The present study sought to establish the relationship between AI task scaffolding, AI verification literacy, and cognitive offloading tendency in terms of their impacts on critical thinking and technical problem-solving of learners, with metacognitive self-regulation as the mediating factor. A cross-sectional design was employed using data from 533 university students across Ghana. Structural equation modeling (SEM) with bias-corrected bootstrapping in AMOS (version 23) was used to estimate the hypothesized relationships. The results indicate that AI task scaffolding is positively associated with both critical thinking and technical problem-solving, whereas cognitive offloading tendency is negatively associated with these outcomes. AI verification literacy does not directly predict the dependent variables but exerts indirect effects through metacognitive self-regulation. These results suggest that the educational value associated with AI-supported learning depends on how students engage with AI systems. The study contributes to the literature by shifting attention from AI adoption to AI interaction mechanisms and highlighting metacognitive self-regulation as the key pathway linking AI-supported learning to higher-order cognitive outcomes.

Purpose: This study examined the influence of metacognitive awareness, epistemological beliefs, and mathematical resilience on university students’ perceived mathematical problem-solving ability and explored the cognitive and affective processes students employ when solving mathematical problems. Design/methodology/approach: An explanatory mixed-methods design was employed. Quantitative data were collected from 350 undergraduate Mathematics Education students at a public university in Ghana using a structured questionnaire and analyzed using exploratory factor analysis, confirmatory factor analysis, and structural equation modeling. Qualitative data were obtained from 10 purposively selected students and analysed thematically to provide further insight into students’ problem-solving processes. Findings: Metacognitive awareness (β = .247, p < .001) and epistemological beliefs (β = .273, p < .001) significantly predicted perceived mathematical problem-solving ability, whereas mathematical resilience did not significantly predict perceived mathematical problem-solving ability (β = .105, p = .163). The qualitative findings indicated that students engaged in problem interpretation and strategy planning, monitoring and evaluation, persistence and strategy adjustment, and experienced varied emotional responses during problem solving. Practical implications: Mathematics instruction should incorporate activities that strengthen students’ metacognitive regulation and productive epistemological beliefs while developing resilience alongside mathematical knowledge and problem-solving strategies. Originality/value: The study combines structural evidence on cognitive and epistemological predictors of mathematical problem-solving with qualitative accounts of students’ cognitive and affective experiences, providing a more comprehensive account of problem-solving among university mathematics education students.

This study examines how AI Credential Institutionalization influences Teaching Innovation and Perceived Student Learning Outcomes through parallel mediating mechanisms, including Institutional Support for AI Credentials, Faculty AI Pedagogical Readiness, and AI-enhanced teaching practices. Employing a quantitative cross-sectional study approach, the study collected data from 375 faculty members and officials who participated in AI credential programs at the selected Ghanaian Universities. A covariance-based structural equation model approach was used to analyze the hypothesized model to determine the implications of AI credential institutionalization on teaching innovations and perceptions of student learning outcomes. According to the study results, the impacts of AI credential institutionalization are found to be significant and direct on both teaching innovations and perceptions of student learning outcomes. The findings indicate that AI Credential Institutionalization is positively associated with both teaching innovation and perceived student learning outcomes. Institutional support emerged as the primary mechanism linking institutionalization with teaching innovation, whereas faculty readiness and AI-enhanced teaching practices were more strongly associated with perceived student learning outcomes. The study contributes to AI-in-higher-education research by shifting attention from individual technology adoption toward institutional conditions shaping sustainable pedagogical transformation.

The purpose of this study is to examine the structural relationships between metacognitive awareness, epistemological beliefs, mathematical resilience, student engagement, and perceived mathematical problem-solving ability among university students. A cross-sectional research design survey was used to examine the research objectives. A total of 350 participants from a population of 2,950 undergraduate students, selected using the Krejcie and Morgan table, were included in the analysis. Exploratory factor analysis with reliability analysis in SPSS software version 27 and hypothesis testing in AMOS software version 23 were used for the analysis. The results indicate that epistemological beliefs, metacognitive awareness, and mathematical resilience are indirectly associated with perceived mathematical problem-solving ability through student engagement. The results indicate that student engagement plays a critical role in mediating the relationship between internal learner characteristics and perceived mathematical problem-solving ability. Practical implications: Mathematics educators should design instructional strategies that strengthen students' metacognitive awareness, epistemological beliefs, and mathematical resilience while creating learning environments that promote active student engagement. Such approaches may help improve students' perceived mathematical problem-solving ability. Teachers can design the learning environment to promote student engagement to improve the students' perceived mathematical problem-solving ability. Although previous studies have examined metacognitive awareness, epistemological beliefs, and mathematical resilience separately or in different combinations, little is known about how these constructs jointly relate to perceived mathematical problem-solving ability through student engagement within a single structural model. To the best of our knowledge, no previous study has simultaneously examined these relationships among university mathematics students in the Ghanaian context.

Difficulties in connecting visual representations with formal deductive reasoning continue to hinder students’ success in geometry proof construction. This study examined the association between generative artificial intelligence (AI)-supported instruction and Senior High School students’ geometric reasoning and proof construction. A quasi-experimental pre-test-post-test non-equivalent control group design was employed involving 86 students from two intact classes in a public senior high school in Ghana. The experimental group received generative AI-supported instruction, while the control group received conventional instruction. Data were collected using a Geometry Reasoning and Proof Test and a perception and attitude questionnaire. Independent-samples t-tests, paired-samples t-tests, ANCOVA, and descriptive statistics were used for data analysis. The findings revealed that students exposed to generative AI-supported instruction achieved significantly higher geometric reasoning and proof construction scores than students receiving conventional instruction, even after controlling for pre-test performance. Students in the experimental group also reported favorable perceptions and attitudes toward the instructional approach. The study contributes to the growing literature on AI-supported mathematics learning by demonstrating the potential of generative AI to function as a cognitive scaffold that supports the transition from visual intuition to deductive reasoning in geometry. The findings suggest that generative AI-supported instruction may provide a useful instructional approach for enhancing students’ geometric reasoning and proof construction.