Electroencephalography distinguished anti-N-methyl-D-aspartate receptor encephalitis and Creutzfeldt-Jakob disease.

Technology-supported learning has become a routine feature of higher education. Digital platforms, online resources, intelligent learning tools, and blended instructional systems, including small private online courses (SPOCs), influence how students organize study time, access feedback, and move through course materials. These developments have encouraged researchers to examine whether technology supported learning is linked to academic performance, self-regulated learning, and student motivation (; ). Yet the meaning of faster learning progress remains theoretically ambiguous. Students may complete tasks more quickly, perceive themselves as moving ahead, or show objective improvements in achievement. These outcomes are related but not identical. Recent research on artificial intelligence and educational technology similarly shows that digital tools increasingly reshape students’ learning routines, engagement patterns, and expectations of timely academic progress (; ). Studies of generative AI and student facing learning systems further suggest that technology use should be examined not only as access to resources, but also as a perceived acceleration of learning opportunities (; ). Blended learning research likewise indicates that technology becomes educationally meaningful when it is integrated with instructional design rather than treated as a delivery channel alone (). The present study focuses on perceived academic acceleration. This construct refers to students’ self-reported sense that they complete learning tasks, progress through academic materials, and move toward learning goals at a faster pace. It is not treated as objective achievement, grade improvement, or formal acceleration placement. This conceptual clarification is important because a cross sectional self-report survey can speak to perceived acceleration, but it cannot establish actual learning speed or causal academic advancement. By aligning the title, measures, results, and interpretation around perceived academic acceleration, the study avoids conflating aspiration, achievement, and task completion speed. This distinction is important because contemporary learning technologies often make progress feel faster through immediate feedback, search assistance, automated explanation, and human AI content creation, even when objective achievement is not directly measured (; ). Evidence from AI assisted writing and chatbot research also indicates that students’ subjective experience of pace can differ from conventional indicators of performance (; ). Prior research has shown that technology use alone is not sufficient to explain learning outcomes. Students’ beliefs about their own learning capability shape how they respond to digital tools, academic challenges, and feedback opportunities (; ). Self-efficacy is therefore a plausible psychological interface between technology enhanced learning frequency and perceived academic acceleration. Students who frequently use digital learning tools may gain more opportunities to practice, monitor progress, and obtain feedback. These experiences may strengthen efficacy beliefs, which are then associated with stronger perceptions of learning progress. Recent studies have extended this logic to AI supported and digital learning contexts, showing that self-efficacy is closely connected to online engagement, technology acceptance, and students’ perceived capacity to use learning tools productively (; ). Research on achievement motivation and academic performance also suggests that efficacy beliefs can translate learning opportunities into more favorable academic appraisals (; ). Technology enhanced learning frequency captures how often students use digital platforms, online materials, and learning tools as part of their academic work. Frequent use may increase exposure to instructional resources, feedback, examples, and practice opportunities, yet technology use becomes educationally meaningful only when students actively employ these resources to organize learning, solve academic problems, and maintain engagement. Systematic reviews of AI in online higher education indicate that technology effects vary across learning design, learner agency, and instructional support rather than following automatically from exposure alone (; ). Immersive and AI enabled systems may support academic success when they create meaningful interaction with learning tasks rather than merely increasing screen time (; ). Within this context, self-efficacy provides a theoretically important mechanism because it helps explain why similar technologies may be experienced differently by different students. This perspective is consistent with research on hybrid human AI learning technologies emphasizing learner agency and self-regulated engagement (; ), as well as broader self-regulated learning theories that view beliefs, monitoring, and strategy use as mutually reinforcing processes (). Learning motivation may also shape how students tra