Visual impairment should not hinder an individual from achieving their aspirations, nor should it be a hindrance to their contributions to society. The age in which persons with disabilities were treated unfairly is long gone, and individuals with disabilities are productive members of society nowadays, especially when they receive the right education and are given the right tools to succeed. Thus, it is imperative to integrate the latest technologies into devices and software that could assist persons with disabilities. The Internet of Things (IoT), artificial intelligence (AI), and Deep Learning (ML)/deep learning (DL) are technologies that have gained momentum over the past decade and could be integrated to assist persons with disabilities—visually impaired individuals. In this paper, we propose an IoT-based system that can fit on the ring finger and can simulate the real-life experience of a visually impaired person. The system can learn and translate Arabic and English braille into audio using deep learning techniques enhanced with transfer learning. The system is developed to assist both visually impaired individuals and their family members in learning braille through the use of the ring-based device, which captures a braille image using an embedded camera, recognizes it, and translates it into audio. The recognition of the captured braille image is achieved through a transfer learning-based Convolutional Neural Network (CNN).

This study investigates the perceptions of university students in Pakistan on the use of ChatGPT in learning. It aims to understand the students’ views on the benefits, challenges, and overall impact of ChatGPT on their academic experiences. A quantitative approach was employed, collecting data from 400 students across various universities in Pakistan through a structured questionnaire. The validity and reliability of the questionnaire were ensured through pilot testing and statistical analysis. The study found that while most students perceive ChatGPT as a beneficial tool for improving their learning, significant concerns remain about academic integrity, over-reliance on artificial intelligence (AI), and the accuracy of information provided by ChatGPT. The findings suggest that perceptions vary across demographic factors such as age, gender, and type of university. These findings can benefit policymakers and educational institutions in effectively integrating AI tools such as ChatGPT into educational systems, ensuring that the value is maximized while addressing potential disadvantages. The study contributes to the existing body of knowledge on AI utilization in education, particularly within the context of developing countries.

Definitions of a sustainable higher education focus on the different factors that are critical to the continued existence of the institution, the people it serves, and the surrounding society. If higher education is assumed to be a conduit for the acquisition of knowledge and skills that can contribute to a healthy, ethical, and sustainable society, then it has to be able to induce lasting behavioral change in its primary beneficiaries (i.e., students). In the age of fake news, misrepresentation, and rejection of scientific principles and facts, we identified cognitive operations that are key to scientific reasoning (i.e., apply, analyze and evaluate), and offered sustainable practice to students enrolled in a course devoted to scientific writing. Students were classified as possessing an inclination towards a reproductive mode of learning, which could increase their vulnerability to absorb fabrications and distortions of information. The research first asked whether practice in applying, evaluating, and analyzing induces an information processing change (as measured by the content of scientific writing). Then, it asked whether environmental disruptions (e.g., shifting from face-to-face instruction, a mode familiar to students, to online instruction due to the COVID-19 pandemic) would affect the likelihood of change. We found that this type of practice was an effective propeller of change in students’ scientific reasoning. A disposition towards reproductive learning did not impair scientific reasoning, whereas engagement and practice made a positive contribution. We concluded that behavioral change is blocked by neither the availability of technology, nor the learner’s use, but rather by one’s motivation to make use of opportunities for change. A sudden alteration in the learning environment may create uncertainty but does not substantially alter this motivation. The findings of the present study can be useful to the development of a sustainable education in the Middle East and beyond.

The purpose of this study was to investigate what common errors and misconceptions about fractions exist among Saudi Arabian college students. Moreover, the study aimed at investigating the possible explanations for the existence of such misconceptions among students. A researcher developed mathematical test aimed at identifying common errors about fractions as well as short interviews, aimed at understanding the thought process while solving problems on the test, were conducted among a total of 107 (n=107) college students. The findings suggested that the majority of college students in Saudi Arabia hold common misconceptions about fractions and mathematical calculations involving fractions, such as thinking that all fractions are always part of 1 and never greater than 1, and using cross multiplication to solve multiplication problems involving fractions.

Purpose: The present field research aimed to assess whether gender differences exist in academic entitlement among college students of a society in rapid transition from a patriarchal system to one fostering gender equity. It then aimed to determine whether particular dimensions of academic entitlement or a simplified one-factor measure can be used as an early indicator of course-related academic difficulties.Design/methodology/approach: College students completed a questionnaire about academic entitlement. Records of test and assessment performance, as well as attendance, were collected for the first half of an academic semester. Due to the coronavirus disease 2019 (COVID-19) pandemic, instruction was delivered online synchronously.Findings: A few gender differences were found that departed from the largely consistent finding in the extant literature of greater academic entitlement in males. For female students, selected dimensions of academic entitlement, but not class attendance, weakly predicted poor performance. For male students, dimensions of academic entitlement were ineffective predictors of both performance and attendance, thereby suggesting that demographic characteristics may define the sensitivity of academic entitlement measures to early, course-related academic difficulties.Research limitations/implications: The current study has limitations to be addressed in future research. For instance, the insensitivity of male students' academic entitlement to early performance measures needs to be investigated further. It may be the byproduct of the weakening of the view of male superiority typical of patriarchal societies, which arises from top-down interventions intended to promote gender equity. Yet, it may also underline males' reluctance to express a view that is counter to institutional messages of gender equity as well as religious and cultural values of modesty (Al-Absi, 2018), and personal responsibility (Asrorovna, 2020; Smither and Khorsandi, 2009). Another limitation is the extent to which specific items or dimensions of academic entitlement map into specific cultural dimensions, such as individualism and collectivism, power distance, uncertainty avoidance, and masculinity and femininity (Hofstede, 2011).Practical implications: Academic entitlement is of concern to educators at universities and colleges across the world for several reasons. High levels of stress (Barton and Hirsch, 2016) and frustration (Anderson et al., 2013) usually accompany academic entitlement along with students' self-reports of lower exerted effort (Kopp et al., 2011). In self-reports, academic entitlement is found to be correlated with an external locus of control, lower academic motivation and lower ratings of class attendance (Fromuth et al., 2019). Boswell (2012) has also reported it as linked to lower students' confidence in their ability to complete with success the courses in which they are enrolled (i.e. course self-efficacy). Thus, the availability of early measures of academic risk in core courses, which prepare students for advanced courses in their major, can be particularly valuable to educators and administrators.Social implications: The Kingdom of Saudi Arabia (KSA) represents a society in rapid transition (Nowak and Vallacher, 2019) from a gender-segregated system to one in which gender equity in educational as well as occupational opportunities and choices is promoted and sustained through top-down institutional changes (e.g. decrees, declarations, investments, etc.; Mansyuroh, 2019). The study’s findings suggest that college students, the main targets of institutional efforts promoting gender equity, may be responsive to such efforts.Originality/value: The present study is a snapshot of a moment at which the impact of top-down institutional actions for gender equity has become palpable mostly due to its spreading across the large youth population of KSA (Assaad and Roudi-Fahimi, 2007). In this research, the authors asked how the target recipients (i.e. college students) of institutional gender-equity efforts might react.

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations.