Review Article
Edmund De Leon Evangelista, Syed M. Salman Bukhari
CONT ED TECHNOLOGY, Volume 18, Issue 4, Article No: ep694
ABSTRACT
Generative artificial intelligence (GenAI) is increasingly influencing how students write, create, reflect, solve problems, and engage with learning in higher education. However, the evidence remains divided, particularly regarding motivation, engagement, creativity, perception, self-regulation, and psychologically informed learning design. This study presents a PRISMA-ScR-based scoping review of GenAI in higher education from a motivational and psychological perspective. The final corpus included 195 studies published between 2023 and 2025 within a specified eligibility window of January 2021 to December 2025. Records were dual screened, coded using a structured framework, and analyzed across psychological focus, thematic constructs, GenAI tools, study designs, outcomes, contextual characteristics, self-determination theory (SDT) needs, equity, and evidence strength. The screening agreement was 81.9%, with Cohen’s κ = 0.642. Motivation was the most common primary focus (31.3%), followed by perception (26.7%), engagement (18.5%), creativity (15.4%), and general/other (8.2%). The literature was dominated by ChatGPT and general GenAI systems, while survey-based and observational evidence was far more common than experimental evidence, longitudinal research, or studies reporting objective performance outcomes. Among 155 empirical studies, mixed or conditional outcomes were most frequent (46.5%), compared with predominantly positive findings (21.9%). SDT related evidence was most common for competence (42.6%), followed by autonomy (29.7%) and relatedness (22.6%), although direct measurement was rare. Based on these findings, the study proposes the GenAI engagement framework, a literature informed structure that organizes five recurring components of GenAI supported learning without assuming a fixed sequence or causal pathway. The framework is not presented as a validated causal model, but as a structured basis for future research and instructional design. The findings suggest that the educational value of GenAI depends less on the technology itself than on how it is guided, evaluated, and integrated into meaningful learning activities.
Keywords: generative artificial intelligence, student motivation, learner engagement, self-determination theory, higher education, educational psychology, multimodal learning, AI-enhanced learning
Research Article
Nailya R. Salikhova, Martin F. Lynch, Albina B. Salikhova
CONT ED TECHNOLOGY, Volume 12, Issue 2, Article No: ep280
ABSTRACT
The purpose of this article was to compile a general map of existing research on digital education from the Self-determination theory (SDT) perspective, in order to understand SDT’s contribution to the emerging field of research on digital technologies in education, the methods used to advance this research, the gaps in existing research, and the development of the theory itself in this context. Methods include searching in databases or search engines and chaining from known research papers. Papers were classed as relevant if their primary focus was to explore the Self-determination theory perspective for digital education. Articles published over the past twelve years in leading scientific journals were analyzed and synthesized. Results show that this theory is actively used both in studies on digital education and in the development of training programs. It makes a significant contribution to solving the problem of continuing digital learning and its motivation, to predicting the academic success of students, to increasing teachers’ motivation to use digital resources. The ideas of SDT have become an important reference point in various formats of digital education: MOOC, hybrid virtual classes, mobile applications, etc. The study found that digital education technologies provide many opportunities to satisfy the need for autonomy whereas they pose the greatest challenge to the need for relatedness. Research in the context of digital education provides new perspectives for the development of SDT, clarifying the relationships of basic needs among themselves. The materials presented in the article are useful for planning further research from the point of view of SDT, as well as for use in the development of digital educational resources. The scientific novelty of this study is to collate, highlight and generalize the directions of application of Self-determination theory in the rapidly developing field of digital education. As an original result, a new general map of the main areas of such research has been created. The review categorizes the literature into five different areas: predicting motivation and intentions to continue digital learning, predicting student academic success, combining SDT ideas with other theories in digital education research, application of SDT for creating online courses, and teachers’ readiness to use digital education.
Keywords: self-determination theory, internal motivation, online learning, digital education