Purpose: This research systematically examines how AI technologies in higher education contribute to achieving Sustainable Development Goals (SDGs), focusing on SDG 4 (quality education) and SDG 10 (reduced inequalities). Design/methodology/approach: We included peer-reviewed articles published in English between 2015 and 2025, identified through ERIC, Web of Science, Education Source Ultimate, Sustainability Reference Center and Academic Search Ultimate databases. Out of the 1,673 records, 1,360 were screened after 313 duplicates were removed. We conducted full-text reviews on 75 eligible articles and 51 met the inclusion criteria. Findings: The findings indicate that AI advances quality education through personalized learning, predictive analytics and adaptive systems, while promoting reduced inequalities via accessibility and inclusive support for underrepresented groups. Key challenges include ethical concerns, infrastructure gaps and cultural resistance. Research limitations/implications: This review is limited by its focus on English-language, peer-reviewed articles published between 2015 and 2025, potentially excluding relevant studies in other languages and publication types. Practical implications: Our review highlights the need for equitable AI implementation in HEIs by promoting ethical frameworks, infrastructure investment and educator training to maximize its potential for advancing SDGs. Originality/value: This study is the first to have a broad view of the application of AI in higher education institutions (HEIs) in the effort of achieving SDGs 4 and 10. It reveals the scope, patterns and implications of AI use and synthesizes barriers impacting its integration and their influence on fulfilling SDG 4 and SDG 10 objectives.

Mental rotation is an important aspect of spatial ability. While the importance of measuring mental rotation has been explored, disputes still exist within the literature surrounding sources of item difficulty in mental rotation tests (MRTs). Furthermore, gender differences in MRT performance are often seen but not fully understood. In the current study, we analyzed sources of item difficulty in a set of spatial ability test items using the Linear Logistic Test Model (LLTM). We found that items with more cubes, color differences, and higher rotational complexity tend to be more difficult, whereas items that contain occlusion, a mirrored structure, and a homogenous configuration type tend to be easier. Next, using Differential Component Functioning (DCF) analysis, we analyzed gender differences across these different item characteristics, finding that the number of cubes and color characteristics made questions more difficult for males when compared to females. The results and implications of this study are discussed in further detail.

Item biasness analysis is an important element in assessing educational test fairness and validity. The study examined the occurrence of item biasness in Lagos State Terminal Unified Mathematics assessment for primary schools pupils. The assessment contained 40 items of multiple choice which was developed by the examination unit of Universal Basic Education Board, Lagos State using primary 5 mathematics curriculum. Primary data of scores of 2018 primary 5 Mathematics 2^nd Term Unified Examination was used for this research. A sample of 640 pupils selected through multistage sampling technique was used in the study. Two research questions guided this study. Data was analysed using descriptive and inferential statistics. The results displayed closeness of the mean and standard deviation scores for examinees groups, indicating the examinees have similar ability levels. Out of the items 16 and 17 functions differentially among the examinees based on gender and age while differential item functioning cannot be estimated based on location because it was found to comprise examinee with different ability level. The results displayed a high level of DIF. Base on the findings, it can be concluded that the examination is not free from differentially functioning items and the overall fairness shows biasness in the items. Consequences and recommendations of the study were advanced.

This study applied Rasch modeling to evaluate the psychometric properties of three spatial assessments Mental Rotation Test (MRT), Object Assembly (OA), and Surface Development (SD) and to examine potential demographic differences and differential item functioning (DIF). Participants were 123 students from Grades 3–8 enrolled in a university-affiliated enrichment program. Item-level analyses indicated acceptable model–data fit (infit MSQ ≈ 0.70–1.30), with Wright maps showing good targeting of items to mid-range ability levels. Gender and race comparisons based on Rasch/EAP theta scores revealed largely overlapping ability distributions and small, nonsignificant differences across tasks. However, a Rasch tree analysis detected significant DIF by gender (p < .001) and by race within males (p = .002). Directional inspection of item parameters showed that several items were relatively easier for females on MRT and OA, whereas a subset favored males on SD. Within males, items showing race-based DIF tended to favor Non-White participants. These findings highlight the value of combining latent-scale analyses with recursive partitioning to detect and interpret subgroup-specific item bias, supporting fairer measurement of spatial ability across diverse populations.

Physical Chemistry concept is a major areas students found difficult. The study adopted correlational design. Twenty Public Senior Secondary schools from four local government areas in Education District III of Lagos state were randomly selected with Five hundred and seven students (507) samples. Physical Chemistry concept difficulty scale (r = 0.76), student Chemistry interest questionnaire (r = -0.87), Numerical ability test (r = 0.79) and Physical Chemistry Achievement Test (r = 0.83) were used. They were validated using face and content validity, while Ordinal alpha, and Kr-20 were used for reliability. Frequency, Percentage, PPMC and multiple regression at p ˂ 0.05 was used. The results revealed that 61% of the students find the physical concepts difficult. Results also showed a low positive relationship between concept difficulty and achievement in Physical Chemistry and no significant relationship between numerical ability and students. There is a relative significant contribution of concept difficulty () on Physical Chemistry achievement while students’ numerical ability () and students’ interest () are not significant in the study. It could be concluded that concept difficulty is a potent predictor of students’ Physical Chemistry achievement. Therefore, Chemistry teachers should improve in their method of teaching concept difficult topics in Chemistry.