Electronic Journal of e-Learning

The Role of Learning Motivation Factors in Deepseek Generative AI Adoption among Higher Education Students in India

Ravi Sankar Pasupuleti — 2025-10-13

This research explores adoption of the Deepseek, an artificial intelligence (AI) platform among higher education students in India by integrating the Technology Acceptance Model (TAM) with learning motivation factors. Given the rapid rise of AI-based platforms in educational sector, understanding their adoption is not only timely but also essential for ensuring equitable and effective learning outcomes. Addressing a critical research gap in understanding of rapidly evolving EdTech sector, the research blends constructs such as learning interest, achievement goals, self-efficacy, and subjective norms in expanding the typical TAM model. This integrative approach allows for a more holistic framework that captures both technological perceptions and learner-driven motivational factors, making the model especially relevant in emerging economies where educational technology adoption varies widely. Data were gathered using an online survey via Google Forms, providing 346 valid responses from students. The sample consisted of students from diverse academic disciplines, ensures representativeness across different fields of study and thereby enhancing the generalizability of the results. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS-3 software. The findings support the extended TAM model which indicated that learning interest and achievement goals have significant impact on perceived ease of use. Self-efficacy and subjective norms have significant impact on perceived usefulness and behavioral intention has significant impact on actual usage, demonstrating its pivotal role in technology adoption. These relationships suggest that motivation-related constructs are not peripheral but central in shaping how students interact with AI-powered platforms. This study advances the literature on educational technology by establishing a new TAM model as applied to AI-powered learning tools in emerging economies. The practical implications are that developers of Deepseek need to make the platform more user-centered in order to increase adoption. Future research avenues involve analyzing other contextual factors and longitudinal patterns of adoption over time. These findings provide useful insights for stakeholders who want to maximize AI learning tool integration in universities.

Developing Conscious Information Consumption Skills in the Age of Digital Change and Media Technologies

Mariia Plotnikova — 2025-10-14

Relevance. The need to study media literacy is driven by the growing destructive influence of information, which is transforming public consciousness and increasing the risk of manipulation in the digital environment. Declining trust in the media, the spread of disinformation, and the growing role of social networks as the main source of news require an in-depth analysis of new communication threats and the search for effective means to overcome them, especially in the field of education. Therefore, the problem of critical thinking among young people, who are at the epicenter of digital interaction and most susceptible to information influence, is becoming increasingly relevant. Aim. The purpose of this scientific article is to empirically measure the levels of media literacy and critical thinking in a social sample, as well as to identify the statistically significant impact of variables such as age, level of education, and participation in media education programs on the formation of relevant cognitive competencies. Methods. During the study, a sociological survey was conducted among Ukrainian citizens in 2024-2025 using a structured online questionnaire, which allowed us to form the initial data for further analysis. Based on this data, the hypotheses were tested using the classic Student's t-test for independent samples with the JASP software package (in particular, the “Descriptives” and “Independent Samples T-Test” tools). Results. The results of the analysis revealed that taking media education courses has a statistically significant impact on the level of media literacy (M = 32.915 and 20.910) and critical thinking (M = 33.111 and 21.059) among respondents, as evidenced by a t-test with a high Cohen's effect (d ≈ -1.57, p < 0.001). The educational level of respondents has a significant impact on cognitive indicators, in particular, respondents with higher education had significantly higher mean values of media literacy (M = 30.023 and 18.473 at p < 0.001) and critical thinking (M = 30.556 and 18.871 at p < 0.001). In addition, a statistically significant age differentiation was found in the levels of media literacy (Group 0: M = 29.904, SD = 6.301; Group 1: M = 20.448, SD = 9.380) and critical thinking (Group 0: M = 30.201, SD = 6.530; Group 1: M = 20.510; SD = 9.209), as evidenced by high t-statistics (t = -13.244; p < .001) and a large Cohen's effect (d = -1.187), indicating a significant association of younger age with higher levels of cognitive competence. Conclusions. The study revealed a low level of media literacy and critical thinking among certain groups of respondents, which indicates the need to implement systematic educational programs aimed at developing cognitive and socio-communication skills. Importantly, the findings highlight the potential of e-learning as an effective environment for media literacy development, since online courses and digital learning platforms can flexibly adapt content for different age and educational groups. This supports the practical integration of media literacy modules into formal and informal e-learning formats, demonstrating how digital tools enhance accessibility and personalization of training. Furthermore, the results reveal a shift in e-learning theory, showing that the effectiveness of online learning in this domain depends not only on access to information but also on the design of interactive, competence-oriented educational interventions. Thus, the study contributes to both the practice and theory of e-learning by identifying critical factors for structuring digital courses that strengthen resilience against disinformation and manipulation. At the same time, the lack of a deeper analysis of socio-cultural factors is a limitation, which opens prospects for further interdisciplinary research in this area.

Toward a Unified Framework for Evaluating Online Education Quality

Ibrahim Tanko Gampine — 2025-10-22

E-Learning has become a global phenomenon. It makes learning more accessible and acquisition of new skills and knowledge easier. In sub-Saharan Africa, however, online qualifications are often the subject of controversy regarding their recognition. This is clear evidence of unsuitable e-Learning systems, as well as the limited relevance of the programs they offered in addressing the Africans’ context-specific needs. Despite the multitude of studies on the quality of online education, inconsistencies in findings make not only comparisons between studies difficult but also complicate the assessment of quality online education. To address this issue, this study integrated the Kirkpatrick with DeLone & McLean models to identify core quality dimensions. Furthermore, this study clarified the context-specific requirements of the identified dimensions. Ten hypotheses were tested using online survey questionnaires administered to four higher education institutions via Qualtrics. The findings supported eight hypotheses and rejected two. This model highlights the critical role played by system quality, the quality of course content, faculty and institutional support in enhancing learning. Furthermore, the model establishes a clear cause-and-effect pathway useful in addressing poor learning outcomes. We discussed the implications of the findings in the context of sub-Saharan Africa. The model is simple, theoretically sound, and comprehensive for real-life applications. Specifically, this study highlighted the importance of both formative and summative evaluations. Further qualitative studies on the context-specific requirements of the dimensions would be desirable.

Professional Development in Digital Competence for Special Education Teachers: A Systematic Review

Iga Setia Utami — 2025-11-04

Digital competence is increasingly being recognised as a crucial factor in transforming education in the technological era. Various studies have been conducted to identify and develop digital competence improvement programs for teachers. However, there has been a lack of comprehensive synthesis regarding their impact, particularly for special education teachers. This problem is important to explore, given that special education teachers face unique pedagogical challenges when serving students with disabilities. This systematic review aims to address this gap by exploring the implementation of digital competence programs for teachers in special education settings. In particular, this study analysed the characteristics of related publications, the effectiveness of training programs, the training materials expected by teachers, and the instruments used to assess digital competence. This study followed PRISMA guidelines, and a comprehensive search was conducted of the Scopus, ScienceDirect, and ERIC databases. This review synthesised 17 studies from 127 screened articles published between 2014 and 2024. The Inclusion and exclusion criteria were based on language, document type, publication year, research type, and full-text availability. The results indicated that while the interest in teacher digital competence is growing, research specifically targeting special education contexts remains limited. Most program initiatives adopt a one-size-fits-all approach, focusing on general digital tools rather than assistive or adaptive technologies suited to learners with disabilities. Training materials tend to emphasise technical rather than pedagogical and accessibility-related aspects. These findings indicate that there is a misalignment between the content of teacher training and the realities of inclusive digital classrooms. The results of this study provide valuable insights for developing digital competence development programs tailored to the needs of special education teachers. This research contributes to digital learning practice by providing a framework for designing practical digital training customised to special education contexts. It advances the scope of virtual and digital learning by highlighting the specific needs and conditions required for inclusive digital education to thrive.

Agentic RAG for Personalized Learning: Design of an AI-Powered Learning Agent Using Open-Source Small Language Models

Shilpi Taneja — 2025-11-05

This paper presents the design of a personalized learning agent powered by the Agentic RAG technique. The agent can interpret learners’ queries and autonomously decide which tools should be used to generate the most suitable response. When the learner shares an Open Educational Resource (OER) they wish to learn from, the agent first breaks the content into smaller, manageable chunks. These chunks are then indexed sequentially to preserve the natural flow of the text. At the same time, chunks are also converted into vector embeddings that allow semantic retrieval. Depending on the learner’s request, different tools are selected by the agent. For example, when the learner requests learning aids like summaries, quizzes, or flashcards, the agent invokes the corresponding tool. This tool passes the sequentially indexed chunks to a small language model to generate the output. For context-specific queries, another specialized tool that relies on vector indexing and retrieval-augmented generation (RAG), is invoked. Visual question answering is handled by a separate tool that leverages multimodal RAG using a multimodal small language model. This agentic setup improves the accuracy and relevance of responses generated by the agent. To test its agentic behaviour, we probed our agent with a diverse set of questions drawn from four different OERs. We thoroughly examined each response and tracked the tools that got invoked autonomously. We also compared the similarity of summaries produced by our agent against those generated by ChatGPT (GPT-4o) using BERT Score as the evaluation metric. Our findings indicate that the agent consistently selected the appropriate tools and the summaries generated by our agent showed close semantic similarity to those produced by GPT-4o, suggesting that the proposed approach can provide performance reasonably close to a state-of-the-art model. The agent being lightweight resides on learner’s local machine and avoid dependence on cloud-based AI ensuring the privacy of learner’s data. It is affordable as it entirely relies on open source frameworks and small models. As the agent provides personalized support to learners by answering their context-based queries and providing on-demand learning aids, it improves their engagement with the educational content. This research shows that designing agentic AI tools using open-source software to address diverse learning needs is technically and economically feasible as well as educationally valuable.

e-DigCompEdu: Validation of a Framework for Online Higher Education Through a Delphi Panel

Cassio Santos — 2025-11-12

This paper addresses the growing importance of digital competence for higher education professors due to the increasing technology integration in this sector. Existing frameworks, such as the European Framework for the Digital Competence of Educator (DigCompEdu), present limitations for higher education, particularly regarding the use of online and blended learning approaches, immersive technologies, and artificial intelligence. Such limitations motivated the development and validation of the e-DigCompEdu, an extended framework specifically designed for this context. The validation process employed a Delphi panel with international experts in distance education, initially involving 29 participants. The selection of specialists was based on their publication records across 40 high-impact distance education journals, involving the analysis of 25,980 authors. The experts evaluated the extended version of the DigCompEdu, with 12 new competencies, specifically considering three aspects: title and description, related activities, and proficiency levels. Experts were asked to rate the competence adequacy on a five-point scale and to offer qualitative feedback. Results showed overall improved adequacy scores, from the first to the second round, as well as an increasing positive evaluation of the competences relevancy. Although some competences experienced a slight reduction in mean scores, they showed decreased variance, demonstrating greater expert consensus. Ultimately, all 12 new competences were enhanced by expert contributions (qualitative) and subsequently validated (quantitative). The validated e-DigCompEdu framework effectively addresses the digital competence requirements from professors in the online education setting. It provides a robust resource for guiding professional development and informing institutional policies regarding the digital transformation of higher education practices.

Adoption Of Adaptive Gamified Learning Systems: A Push-Pool-Mooring Model Perspective

Eddy Triswanto Setyoadi — 2025-11-12

The adoption of digital learning systems is closely related to user engagement and system relevance. This quantitative research aims to explore the factors influencing students' switching intention from traditional Learning Management Systems (LMS) to a gamified LMS platform, using the Push-Pull-Mooring (PPM) framework. A conceptual model was developed to examine how negative experiences with previous systems (push factors), the appeal of a new gamified platform (pull factors), and personal constraints (mooring factors) influence switching behavior. The gamified LMS, named Learning Nova, was designed based on six types of goal orientation, enabling personalization according to students’ motivational profiles. Data were collected through a two-stage process: an initial classification using a modified AGQ-R questionnaire, followed by a large-scale survey involving 1,054 university students from various institutions across Indonesia who interacted with the prototype. The findings confirmed the significant influence of both push and pull effects on switching intention. While mooring factors did not moderate these effects, they had a direct impact on students’ decisions to switch. These insights offer practical implications for educational institutions and system developers seeking to enhance LMS adoption through motivation-aligned, gamified experiences.

Evaluation of an AI-Based Feedback System for Enhancing Self-Regulated Learning in Digital Education Platforms

Maxot Rakhmetov — 2025-11-13

The development of self-regulated learning (SRL) skills, including the ability to plan, monitor and reflect, is increasingly recognised as essential for academic success in online learning environments. Despite this, most digital learning platforms continue to provide limited feedback, typically focused on task outcomes rather than learning processes. This study investigates the effectiveness of an artificial intelligence-based feedback system integrated into a standard online course platform. The system delivers adaptive, process-oriented feedback by analysing anonymised engagement summaries and short reflective inputs, aiming to promote self-regulated learning strategies without requiring additional instructor involvement or manual input for feedback generation. A quasi-experimental study was conducted with 180 undergraduate students enrolled in a fully online course. Participants were pseudo-randomly assigned by an automated allocation script to an experimental group (n = 90) receiving AI-based adaptive feedback or a control group (n = 90) with standard LMS features. The system employed behavioural indicators (e.g., time-on-task, quiz activity and content engagement) and natural language analysis of reflective entries to generate personalised prompts related to goal-setting (including time management), effort regulation and metacognitive reflection. Data sources included post-course surveys, aggregated system interaction records, academic performance data and open-ended student feedback on the system’s perceived effectiveness and usability. Students who received adaptive feedback exhibited significantly stronger engagement with SRL behaviours, including earlier task initiation, increased use of optional learning resources and greater consistency in study routines. Qualitative responses indicated that participants found the feedback clear, timely, actionable and supportive of their cognitive and motivational processes. In contrast, control group participants primarily relied on grade-based feedback and exhibited fewer strategic adjustments during the course. The findings suggest that a lightweight, AI-driven feedback mechanism can be effectively integrated into online course platforms to support SRL at scale. This study demonstrates how adaptive AI feedback can meaningfully influence academic outcomes, planning behaviour and engagement with feedback in digital learning environments.

Using Learner-Generated Videos to Foster Multimedia Communication Skills in Graduate Health Profession Education

Jennifer Hogg — 2025-10-20

The advent of graduate level athletic training education programs, including those with online didactic curriculum, encourages instructors to incorporate higher level thinking strategies into their curricula. “Create” and “synthesize” are high-level verbs in Bloom’s Taxonomy. Pathomechanics, the study of how musculoskeletal structure and function affect movement patterns, provides a prime opportunity to emphasize higher levels of critical thinking. Because degree programs in the health sciences are heavily “hands-on” and applied, creatively using technology in an online environment to develop transferable skills is critical for such health specialties as athletic training or physical therapy. The purpose of this experiential case study is to describe a method whereby graduate athletic training students are assessed in their ability to create and synthesize information pertaining to structural and gait anomalies. Doing so will allow for empirical work to determine the efficacy of this approach on student learning. Specifically, learner-generated videos may be used as an assessment tool, either in a traditional classroom or in an online classroom. Students report higher levels of active learning, engagement, and acquisition of competencies following creation of video content, which also fosters multimedia manipulation skills and development of 21st century communication ability. Approximately a month prior to the end of the term, the instructor provides students with the instructions for the assignment, each student’s individual gait prompt, and the rubric. A collaborating multimedia librarian instructs the students in the use of technology for video creation and editing. The deliverable product is a video ~1 minute in length of a gait demonstration of the assigned gait prompt, complete with annotations and voice-over explanations of how the studied gait anomaly may influence kinematics and kinetics throughout the body. Learner-generated videos increase active learning, competency acquisition, and multimedia communication skills. Another primary advantage of this assessment is the potential for student-student and instructor-student collaboration and its ability to be a formative iterative assignment. Furthermore, mastery of pathomechanical content requires synthesis of information from anatomy, physiology, and orthopedic assessment courses. Learner-generated videos offer numerous advantages to student engagement and learning and require synthesis of information from across an athletic training curriculum, serving as a compact and comprehensive assessment.