The current study highlights the potential of multimodal large language models (MLLMs) to transform higher education by identifying key factors influencing their acceptance and effectiveness. Aligning technology features with educational needs can enhance student engagement and learning outcomes. The study examined the role of MLLMs in enhancing performance benefits among higher education students, using the task–technology fit (T-TF) theory and the artificial intelligence device use acceptance (AIDUA) model. A structured questionnaire was used to assess the perceptions of 550 Saudi university students from various academic disciplines. The data were analyzed via structural equation modeling (SEM) using SmartPLS 3.0. The findings revealed that social influence negatively affected effort expectancy regarding MLLMs and that hedonic motivation was also negatively related to effort expectancy. The findings revealed that social influence and hedonic motivation negatively affected effort expectancy for MLLMs. Effort expectancy was also negatively associated with T-TF in the learning context. In contrast, task and technology characteristics significantly influenced T-TF, which positively impacted both performance benefits and the willingness to accept the use of MLLMs. A strong relationship was found between adoption willingness and improved performance benefits. The findings empower educators to strategically enhance MLLMs adoption strategically, driving transformative learning outcomes.

The current study aims to investigate the crucial factors that influence the behavioral intentions of students to use distance education platforms (DEP_s). The tested Davis Expanded Technology Acceptance Model (TAM) was applied with Bandura’s social cognitive theory (SCT). This study included 453 university students using DEP_s in the Kingdom of Saudi Arabia. A quantitative approach was adopted, and the results revealed that both perceived ease of use and usefulness have a strong influence on student intentions to use DEP_s. Observational learning and computer self-efficacy also strongly influence perceived ease of use, and learner control with scaffolding was shown to significantly influence perceived usefulness. Moreover, the results confirmed the significant effect of perceived ease of use on perceived usefulness. Accordingly, the results provide valid support for an integrated framework including both TAM and SCT to understand how students may accept DEP_s to improve their educational performance. This integration would help the decision-makers who are responsible for managing these platforms to assess the real needs of students at all higher education institutions.

The integration of artificial intelligence (AI) technologies into education has gained considerable attention for its potential to revolutionize teaching and learning practices. Despite this, the adoption of AI in higher education, particularly to support inclusive learning environments, remains insufficiently studied. This research examines the factors affecting the adoption of AI for collaboration among university students, with a focus on promoting inclusivity. Data was gathered from 443 students at King Faisal University using a survey designed to measure constructs related to AI adoption in collaborative learning. The questionnaire, based on the Technology Acceptance Model (TAM), was tailored to the educational AI context. “Structural equation modeling (SEM)” and hypothesis testing were applied to evaluate the relationships between constructs. The results indicated that “perceived ease of use”, “perceived usefulness”, and “behavioral intention to use AI” are significant predictors of AI adoption among students. Additionally, trust and familiarity with AI were identified as key drivers of behavioral intention. However, the hypothesis that the perceived quality of AI output influences “perceived usefulness” was not supported, suggesting students may not strongly link the quality of AI outputs to its usefulness in academic contexts. This study adds to the body of literature by providing empirical insights into the factors influencing AI adoption for collaboration, offering practical recommendations for creating more inclusive and effective educational environments. The study highlights the importance of building student trust and familiarity with AI to enhance its adoption for inclusive and collaborative learning in higher education.

As an effect of the digital transformation encountered by higher education institutions in the post-pandemic phase, the current study aims to inspect the factors affecting the actual use of mobile learning among higher education students. A novel hybrid model based on the information system success and technology acceptance models was proposed and tested. The study included 400 undergraduate and postgraduate students from four Saudi universities who responded to a questionnaire consisting of two parts and seven dimensions, with a total of 26 items. For the analysis, a quantitative approach was applied using structural equation modeling. The results displayed that information quality had no impact on the actual use of mobile learning among higher education students. In contrast, other quality factors (system quality, service quality, and satisfaction) and perceived factors (perceived usefulness and perceived ease of use) had a positive effect. Accordingly, this study proposed an integrated framework to assist decision makers at higher education institutions in scaffolding students to develop their educational performance by depending on mobile applications comprising high-quality factors that address their real needs. This would also enable higher education institutions to enhance their digital transformation experience, thus contributing to achieving positive learning sustainability after the pandemic.

Generative AI (GenAI) is reshaping academic writing and research workflows, especially for scholars publishing in a language other than their own. This qualitative case study examines how Saudi non-native English-speaking (NNES) researchers use GenAI and how they evaluate its quality and integrity when writing for publication in English. Drawing on 34 semi-structured interviews across disciplines and career stages, analysed via reflexive thematic analysis, we identify five roles that structure GenAI use: literature navigator, writing enhancer, intellectual partner, research designer–analyst, and publishing mentor. The participants reported clear benefits: speed, reduced effort, improved translation and coherence, organized summaries and lower costs, especially in literature work and language polishing. They also stated systematic quality and integrity checks, citing risks of unreliable summaries, fabricated or misattributed references, privacy and copyright concerns and erosion of voice and creativity. Human agency and responsible use are emphasized across practices. Although anchored in the Saudi context, the findings are analytically transferable to systems in which English-language publication is compulsory outside English-dominant environments. The results inform policy and practice: international and local bodies can refine guidance and competency frameworks; institutions and NNES researchers can strategically leverage GenAI while safeguarding integrity and authorial voice; editors of ranked journals can clarify GenAI policies for fairness; and targeted training, especially for postgraduate researchers, can strengthen critical, analytical, ethical and legal GenAI competencies.