The Influence of AI-Powered Learning Tools on Academic Student Performance in Higher Education

Impact of Artificial Intelligence–Assisted Learning Tools on Academic Performance and Study Habits Among Secondary School Students in Nigeria

The integration of Artificial Intelligence (AI)–assisted learning tools into educational environments has transformed students’ learning experiences globally, yet empirical evidence on their impact within the Nigerian secondary school context remains limited. This study investigated the effect of AI-assisted learning tools, such as ChatGPT and similar digital platforms, on academic performance and study habits among secondary school students in Nigeria. The study adopted a descriptive survey research design. A sample of 250 Senior Secondary School II (SS2) students was selected using simple random sampling from selected public secondary schools. Data were collected using a structured questionnaire titled “AI Assisted Learning and Academic Behaviour Questionnaire (AILABQ)” and students’ academic records. The instrument was validated by experts in educational technology and measurement, and a reliability coefficient of 0.82 was obtained using Cronbach’s alpha method. Data were analyzed using mean, standard deviation, and independent t-test statistics. The findings revealed that the use of AI-assisted learning tools significantly improved students’ academic performance and positively influenced their study habits, including personalized learning, time management, and access to instant feedback. However, concerns regarding over-dependence and reduced critical thinking skills were also observed. Based on the findings, it was recommended that educators should integrate AI tools into teaching practices with proper guidelines, while policymakers should develop frameworks to regulate their use in schools. The study contributes to the growing body of literature on digital learning innovations and provides practical implications for enhancing secondary education in Nigeria.

Impact of AI Dependence on Students' Academic Procrastination in Higher Education

Artificial Intelligence (AI) has become an important part of higher education by providing students with quick access to information, personalized learning support, and assistance in completing academic tasks. The increasing use of AI tools has improved learning opportunities; however, concerns have also been raised about students becoming overly dependent on these technologies. Excessive dependence on AI may influence students' learning habits and encourage them to delay academic responsibilities. Therefore, this study examined the impact of AI dependence on students' academic procrastination in higher education. A quantitative research approach was adopted using a cross-sectional survey design. Data were collected from 100 higher education students through a structured questionnaire using a five-point Likert scale. The questionnaire consisted of demographic information, AI dependence items, and academic procrastination items. The collected data were analyzed using the Statistical Package for the Social Sciences (SPSS). Descriptive statistics, reliability analysis, and simple linear regression were used to analyze the data. The findings indicated that AI dependence had a significant positive impact on students' academic procrastination. The study highlights the importance of encouraging students to use AI responsibly while maintaining independent learning and effective time management. The findings may help educators and higher education institutions develop policies and strategies for the ethical and balanced use of AI in academic settings.

Influence of AI-Powered Assistant Tools on Students' Engagement and Critical Thinking in Curriculum Learning in University of Port Harcourt

The rapid integration of Artificial Intelligence (AI)-powered assistant tools into higher education has generated significant scholarly interest, particularly regarding their influence on student engagement and the development of critical thinking skills. This study investigated the influence of AI-powered assistant tools on students' engagement and critical thinking in curriculum learning within the Faculty of Education, University of Port Harcourt, Nigeria. The study adopted a descriptive survey research design; the study targeted a population of 1,406 students from the 2022 and 2023 academic sets. A sample of 343 respondents was selected through purposive, simple random, and proportional stratified random sampling techniques. Data were collected using a structured questionnaire titled (IAATSECQ) with eight items each. Also, an AI assisted quiz was administered via Google Forms. The reliability of the instrument using Cronbach’s alpha was 0.751. Descriptive statistics (mean and standard deviation) were used to answer research questions, while independent samples t-tests were employed to test the hypotheses at a 0.05 significance level. Findings revealed that AI-powered tools significantly enhanced students' engagement. Gender was found to significantly influence engagement (z = 2.985, p = 0.003), with female students demonstrating higher engagement levels. Age significantly influenced critical thinking (z = 5.365, p = 0.000), with younger students (18–25 years) recording higher critical thinking scores than older students (26–35 years). The study concludes that AI-powered assistant tools are effective instruments for enhancing academic engagement and critical thinking in Nigerian higher education, and recommends the institutionalization of AI literacy programmes and differentiated AI integration strategies that are responsive to students' gender and age characteristics.

AI-Enhanced Learning Analytics and Student Performance Prediction at Higher Education Level

In the era of digital transformation, higher education institutions are producing enormous amounts of students' data, but many of them still do not fully utilize that data for educational insight to the benefit of student success. This gap is filled by this study developing and testing a theoretically informed AI-supported learning analytics framework in a pedagogically relevant way that goes beyond algorithmic optimization. The research design used was quantitative with a predictive correlational approach in which 1248 students from various institutes of higher education in Pakistan and abroad were involved. Data consists of LMS interaction logs, attendance records, assessment results, and self-regulation surveys that were validated. The proposed framework was tested using descriptive statistics, structural equation modeling and machine learning approaches for analyzing data. Results showed good predictive accuracy (82.4% for the identification of the at-risk students), and that self-regulated learning behaviors proved to be important mediators. The proportion of children with cycling, walking and active play increased significantly using AI-generated indicators, but demographic discrepancies remained. Practically, it recommends the universities how to implement in a responsible way that can support the value of teaching, student support, institutional decision-making, and ethical norms.

Utilization of Artificial Intelligence on the Learning and Performance of Students: Implications for Teaching Strategy

This study examined the extent of artificial intelligence (AI) utilization and its relationship with the learning performance of junior high school students in the Schools Division of Tacloban City during School Year 2025–2026. It specifically determined the AI platforms used by learners, their frequency and duration of AI use, common academic tasks, AI competence, perceived challenges, and learning performance. It tested the relationship between AI utilization and learning performance. The study used a descriptive-correlational research design with 208 Grade 7 to Grade 10 students selected through stratified random sampling across school size categories. Data were gathered using a validated researcher-developed questionnaire and analyzed using descriptive statistics and Spearman’s rho at the 0.05 and 0.01 levels of significance. Findings showed that ChatGPT was the most commonly used AI platform. Overall AI utilization was moderate, with learners using AI mainly for academic clarification, lesson summarization, and improvement of written work. The learners demonstrated a generally competent level of AI competence, particularly in AI literacy and verification skills, but showed areas for improvement in technical skills and ethical citation practices. They also reported challenges related to misinformation, privacy, and possible overdependence on AI. They rated learning performance in engagement, motivation, productivity, and critical thinking as very satisfactory. AI utilization had significant relationships with selected aspects of learning performance, particularly motivation, task completion, and critical thinking, but showed no significant relationship with overall learning performance. These findings suggest that AI use may be associated with specific learning outcomes but should not be viewed as a direct predictor of overall learning performance. Based on these findings, the study proposed an AI-supported teaching enhancement strategy to promote responsible and effective AI use in learning.

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