This study explains the processes through which a relationship value in customer-owned channels is generated through AI-powered functionalities by focusing on the integrated psychological and behavioral processes. The basis of the work is large-scale text analysis of 18,456 market reviews of applications among 15-20 top customers in the areas of retail, finance, and life services, examining the entire value chain by semantic BERT analysis, hierarchical regression, and structural equation modeling with 5,000 bootstraps. Analysis of the results reveals the presence of function–dimension matching phenomena. These include intelligent recommendation, which has the maximum cognitive engagement (β = 0.41), chatbots, which have the peak affective engagement (β = 0.45), and predictive services, which are predominant in behavioral engagement with a β of 0.36. Customer-owned platforms produce 37.2% more overall effects than third-party platforms. The role of the overall chain mediating function of AI functions for relationship value creation is also supported by the study. The role that loyalty plays between trust and value is amplified by ownership of the channel. The results above present the objective assessment of the 37% boost that the ROI (Return on Investment) has on customer-owned communication channels. It also explains the role that development considerations play.

Grounded in Self-Determination Theory (SDT), this research delves into the effectiveness of integrating process drama into the flipped classroom model to enhance learners' motivation and engagement in an English as a Foreign Language (EFL) speaking course. A mixed-methods study was carried out on 67 first-year English majors over an 8-week experiment. The experimental group underwent drama-based flipped teaching, while the control group received traditional teaching. Subsequently, semi-structured interviews were conducted with 16 students and one course lecturer. Quantitative findings revealed that the combination of process drama and the flipped classroom significantly boosted learners' motivation, as well as their behavioural, emotional, and cognitive engagement, with large effect sizes. Qualitative results indicated that both students and the teacher perceived that the integrated teaching model satisfied students' needs for autonomy through "the freedom of script creation", their needs for competence via "the authentic, low-risk language practice", and their needs for relatedness through "the social bonding". This study empirically validates the efficacy of the drama-based flipped classroom model within the context of Chinese EFL teaching and learning. It also offers practical implications for EFL instruction and establishes a foundation for future research on drama-integrated flipped teaching.

Despite the remarkable progress in the field of artificial intelligence (AI), current models seldom incorporate emotions or affective states, constraining their capacity for emulating truly adaptive and human-like behavior. Boredom is a recurrent affective state that signals a mismatch between available cognitive resources and environmental demands, prompting a state-escape response to restore optimal engagement. Cast within a functional psychological framework, boredom is characterized to arise when the level of cognitive engagement falls outside an optimal range, whether due to under- or overstimulation. Here, we translate this principle into a biologically inspired control loop implemented with spiking neural networks. The model continuously monitors simulated cognitive resource utilization, signals deviations that occur due to perturbations in the input and dynamically influences the utilization of resources to maintain an optimal engagement level. Simulations demonstrate that the model effectively maintains stable cognitive engagement by exciting and inhibiting a spiking neuron population that abstractly represents the processing of input. This work establishes a foundation towards the development of a future model capable of autonomously defining and regulating its optimal level of cognitive engagement. By embedding such affective regulation directly into a spiking architecture, our approach bridges cognitive neuroscience and AI, offering insights into how human-like state monitoring and initiating of corrective actions can be realized within brain-inspired AI. • Modeling boredom as a homeostatic control loop with spiking neural networks. • Successful replication of the boredom regulation mechanism that maintains optimal cognitive resource utilization. • Novel approach for replicating human experiences and affective states in an artificial intelligence model.

During the 2020-2021 school year, U.S. students alternated between in-person and remote learning due to the COVID-19 pandemic. This study investigates whether differences in students' engagement across learning modalities were mediated by positive affect, negative affect, and stress. Using data from 639 adolescents (Grades 7-12) collected via a multiburst daily-diary design (30 days in total), multilevel mediation models were used to examine within-person differences in behavioral, cognitive, emotional, and social engagement. Students reported lower engagement and positive affect on days they attended remote versus in-person learning. Results pertaining to negative affect and stress were nonsignificant. Positive affect partially mediated the association between learning modality and all engagement dimensions. Results suggest that the decrease in engagement associated with remote learning is driven by reduced positive emotional experiences. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The effects of GAI-enhanced pedagogical agents in the metaverse (GPAiM) on elementary school students’ conceptual understanding and cognitive engagement patterns

Most students struggle to write argumentative essays due to a lack of schemata, so resources are needed to enrich them. The purpose of this study was to investigate the impact of SVVR-assisted immersive teaching using a flipped classroom model on argumentative essay writing skills, class engagement, and student perceptions. A quasi-experimental method was used, involving 200 university students majoring in Indonesian language education. The participants were divided into two groups with the same number of 100 students each: the experimental group received the intervention of spherical video virtual reality in a flipped classroom model, while the control group received conventional teaching. The instruments used were a rubric for assessing argumentative writing skills, a class engagement questionnaire, a rubric for assessing the lexical complexity of argumentative essays and interview questions. The data analysis included the Wilcoxon signed-rank test, the Quade test, paired-sample t-test, and one-way analysis of covariance (ANCOVA). The findings indicated that SVVR-assisted immersive teaching with a flipped classroom model improved argumentative essay writing skills, class engagement, and student perceptions. Improved argumentative writing skills were evident in the use of evidence and data to support arguments, which were presented more robustly and scientifically, resulting from the observation of objects. Improvements in the quality of argumentative essays were also evident in the increased lexical complexity across all aspects of lexical density, lexical sophistication, and lexical variety. Increased class engagement was evident in cognitive engagement, behavioral engagement, and emotional engagement. Positive perceptions were evident in the experience, emotion, active motivation, and strategies used for improving writing learning. Thus, improvements in all competencies occurred because the intervention enhanced realistic and context-rich experiences, critical thinking skills, and evidence-based reasoning. This research suggests that the use of virtual reality technology can enhance the students' understanding of difficult concepts and create positive impressions of the learning process.

Background: The rapid integration of Generative Artificial Intelligence (GenAI) in higher education has reshaped how students engage with academic work. While GenAI improves efficiency and accessibility, concerns arise regarding its effects on cognitive engagement and self regulation.Purpose: This study examined how Ease of Internet Access (EIA) and Frequency of GenAI Use (FGAI) influence Learning Motivation (LM), with Academic Procrastination (AP) as a mediating variable.Method: A total of 205 undergraduate students from Politeknik AKA Bogor completed standardized questionnaires adapted to GenAI-related learning. Data were analyzed using multiple regression, mediation analysis based on Baron and Kenny’s framework, and multi-group confirmatory factor analysis (MGCFA).Findings: The results showed EIA and FGAI did not significantly predict LM (R² = .006; p > .05). EIA significantly predicted AP (β = .146, p = .039), and AP negatively predicted LM (β = −.603, p < .001). Mediation analysis confirmed a significant indirect effect of EIA on LM through AP (Sobel = −2.075, p = .038). MGCFA supported configural and metric invariance across GenAI-use groups (ΔCFI = .003), with partial scalar invariance achieved.Implication: These findings indicate that digital accessibility may indirectly reduce motivation by increasing procrastination, emphasizing the importance of self-regulation and guided AI integration in higher education.

ABSTRACT The COVID-19 pandemic marked an unprecedented turning point in global education, accelerating the shift from traditional classroom learning to technology-driven and AI-assisted academic environments. While this transition expanded access to digital resources and online learning platforms, it simultaneously introduced new patterns of student behavior both productive and problematic. This paper presents a conceptual review of how Artificial Intelligence has progressively integrated into student education, examining developments from the pre-COVID digital era through the post-COVID generative AI period spanning 2000 to 2026. Drawing upon existing literature across three distinct phases pre-COVID foundational learning (2000–2019), the pandemic-driven digital shift (2020–2021), and the post-COVID AI adoption era (2022–2026) this study synthesizes key findings related to learning transformation, AI-assisted personalization, cognitive dependency, and academic misuse. The review identifies significant benefits of AI integration including improved accessibility, self-paced learning, and personalized academic support. Simultaneously, it highlights critical concerns such as over-reliance on AI-generated content, reduced independent reasoning, and the misuse of AI tools for academic dishonesty. A conceptual framework is proposed that positions AI as a dual-impact mechanism functioning either as an augmentation tool that enhances student learning or as a dependency driver that undermines intellectual development, depending on the pattern of use and institutional regulation. The study concludes that responsible AI integration in post-COVID education requires structured digital literacy, clear academic policies, and a balanced pedagogical approach that preserves student cognitive engagement. Keywords: Artificial Intelligence in Education, Post-COVID Learning, Student AI Dependency, Academic Integrity, Generative AI, Digital Transformation, Cognitive Offloading

There is a growing interest in elucidating the mechanisms that drive the benefits of physical exercise on cognitive performance. A key element for a better understanding of a particular phenomenon (e.g., the mediators of the exercise-cognition interaction) is the selection of an appropriate control condition/group as the basis for causal inference. In contemporary practice, control conditions/groups used in exercise-cognition research can be broadly categorized as related to (i) study design, (ii) level of energy expenditure, and (iii) level of cognitive engagement. Although such control conditions are valuable for reducing the effects of specific sources of bias (e.g., time or placebo effects), their potential to advance our mechanistic understanding is limited. To address this research gap, the present article proposes and discusses the application of "physiologically informed" control conditions by narratively summarizing the current evidence concerning "physiologically informed" control conditions in acute exercise-cognition studies, wherein specific physical exercise-induced physiological responses (e.g., increases in cerebral blood flow or peripheral blood lactate concentration) are mimicked by nonexercise experimental manipulations (e.g., through inhaling hypercapnic gas mixture or infusion of lactate at rest). Based on our narrative evidence synthesis, we discuss how "physiologically informed" control conditions can serve as a valuable approach to strengthen causal interferences by allowing for a better isolation of cognitive benefits that can be "solely" attributed to specific physical exercise-induced physiological changes. As applying "physiologically informed" control conditions can advance knowledge generation on the physiological mechanisms that drive the positive effects of physical exercise on cognition, we advocate for a more widespread use of these control conditions in future research practice.

Recent large-scale surveys indicate that the use of generative Artificial Intelligence (AI) in higher education has become nearly ubiquitous. A survey in the United Kingdom reported that 92% of university students use generative AI tools in their studies. In contrast, a national Indonesian survey involving 1,501 respondents found that 86.21% use AI to assist with academic tasks at least once a month. Preliminary institutional data further revealed that 68.6% of students reported using AI in nearly every assignment. Such high prevalence suggests that AI use has shifted from occasional assistance to habitual reliance, raising concerns about potential dependency and reduced independent cognitive engagement. Although existing instruments, such as the Cognitive AI Dependence and Interaction Scale (CAIDS), assess attitudes and general interaction patterns toward AI, they do not specifically measure functional and cognitive dependency in academic task completion. Therefore, this study aimed to develop and validate an AI task completion dependency scale for university students. A psychometric scale development design was employed involving 500 students from several universities in Indonesia who reported using AI to complete academic assignments. Data were analyzed using exploratory factor analysis (EFA), confirmatory factor analysis (CFA), Rasch modeling, and differential item functioning (DIF) analysis based on gender. The findings revealed a stable three-factor structure comprising functional dependency on AI, reflective attitude, and independent use, and regulation and critical evaluation of AI, yielding a final 10-item scale. The results indicated preliminary structural support for the three-factor model, acceptable reliability, strong item functioning based on Rasch analysis, and acceptable gender invariance. Overall, the developed instrument provides a valid and reliable measure of students’ dependency on AI in academic task completion and offers a practical tool for evaluating and managing responsible AI use in higher education contexts. Keywords: academic task completion , AI dependency , indonesian students , rasch model , scale development.

The integration of digital technologies into classroom instruction has become a defining feature of contemporary secondary education. Schools increasingly adopt digital tools, smart classrooms and technology-supported learning platforms to enhance teaching practices and improve student learning outcomes. This study examines how technology integration in classroom instruction influences student engagement and academic achievement in secondary education. The research adopted a qualitative secondary research design based on the systematic analysis of peer-reviewed studies published between 2020 and 2025. Ten empirical studies focusing on technology-enhanced learning environments, teacher digital competence and student engagement were analysed using thematic synthesis. The analysis identified five major themes: technology-supported student engagement, teacher digital competence and pedagogical mediation, smart classrooms and digital learning environments, technology integration and learning outcomes, and learner agency and self-regulated learning. The results show that technology can enhance behavioural, cognitive and motivational engagement when it is integrated into meaningful learning activities. The findings also reveal that the effectiveness of classroom technology depends strongly on teachers’ digital competence, classroom interaction and student-centred instructional approaches. Furthermore, technology-supported learning environments appear to contribute to academic achievement, collaboration and the development of digital competencies when learners actively participate in the learning process. The study concludes that the impact of technology integration in secondary education depends less on the presence of digital devices and more on the quality of pedagogy, teacher support and learner engagement within technology-enhanced classrooms.

This study examined the potential of rebus-based instruction as a visually enriched pedagogical approach for supporting gifted students’ learning of Mesopotamian and Anatolian civilizations. Within the framework of sustainable education, the study focused on instructional practices that promote meaningful learning, active cognitive engagement, and the short-term retention of knowledge, particularly in relation to cultural and historical understanding. A mixed-methods research design was employed. Quantitative data were collected through a pre-test–post-test quasi-experimental design, while qualitative data were obtained through semi-structured interviews. The study group consisted of 18 gifted students enrolled in a Science and Art Center (BILSEM) in Istanbul during the 2024–2025 academic year. Of these, 11 students were assigned to the experimental group and 7 to the control group. The experimental group received rebus-based instruction, whereas the control group was taught using traditional teaching methods. Quantitative findings showed a statistically significant improvement in the academic achievement scores of the students in the experimental group compared with their pre-test scores. Qualitative findings further indicated that rebus-based instruction enhanced students’ motivation, creativity, visual perception, and analytical thinking, while also supporting meaningful learning and short-term recall over a one-month interval. In addition, students demonstrated an improved ability to recall and accurately identify historical civilizations. Overall, the findings suggest that rebus-based instruction may be a promising pedagogical approach for promoting engaging and meaningful learning experiences and for supporting the teaching of cultural and historical knowledge in gifted education.

This paper aims to examine the causal impact of self-chosen performance, learning and behavioral goals on student motivation and academic performance in a college Microeconomics course. Using a randomized controlled trial, students were assigned to one of four groups: a control group with no specific goals or treatment groups that self-select their own performance, learning or behavioral goals. We find that self-chosen goals enhance academic outcomes compared to a control group, with learning and behavioral goals yielding significant effects. Performance goals drive higher achievement when paired with strong goal commitment, learning goals facilitate deeper cognitive engagement and behavioral goals encourage consistent and productive study habits.

Purpose: This study presents a systematic literature review examining mathematics self-efficacy research within the Indonesian educational context from 2019 to 2024. Methodology: Guided by the PRISMA 2020 protocol, fifty-seven peer-reviewed journal articles were identified through the GARUDA database, meeting strict inclusion and exclusion criteria. Data were analyzed to determine frequently used keywords, primary research themes, and temporal trends. Keyword analysis revealed a predominant focus on constructs such as learning outcomes, problem-solving ability, motivation, and academic achievement, indicating a learner-centered orientation. Findings: Thematic synthesis demonstrated that most studies investigated the impact of self-efficacy on mathematics achievement, with fewer addressing affective factors, conceptual understanding, or higher-order thinking skills. Temporal analysis indicated a publication peak in 2021, followed by a decline, potentially linked to shifts in theoretical frameworks and pandemic-related disruptions. Significance: Findings underscore the critical role of self-efficacy in shaping students’ cognitive, affective, and behavioral engagement in mathematics, and call for more diverse methodological approaches and participant demographics in future research.

ABSTRACT Background to the Study As fully online postgraduate programmes expand, questions remain regarding whether sufficient student engagement is achieved and how such sufficiency can be measured. This study examined the types and levels of engagement within a fully online postgraduate module and explored how engagement can be operationalised using learning management system (LMS) analytics. Objective To explore whether there is sufficient student engagement in an online module, and the types and levels of online engagement. Methods A quantitative single‐case study analysed LMS trace data from 773 students. Data were analysed using the Online Engagement Framework and Moore's interaction typology. Engagement was operationalised using four behavioural indicators: submissions, interactions, time‐on‐platform and Grade Center access. Cluster analysis was applied to identify engagement profiles. Results Findings indicate high levels of social, cognitive, behavioural and collaborative engagement, with participation substantially exceeding minimum requirements. In contrast, structured opportunities for emotional engagement were absent. Frequent Grade Centre access (mean = 68 views per student) suggests a digitally observable form of performance engagement characterised by academic self‐monitoring behaviour Cluster analysis revealed four distinct engagement profiles, highlighting heterogeneity in student interaction patterns. Conclusion The findings suggest that high‐density programmatic assessment is associated with sustained engagement behaviours in online contexts. This study contributes to the literature by proposing a trace‐based operationalisation of performance engagement and offering a practical framework for examining engagement sufficiency in fully online programmes.

Instructional design benefits from integrating models like Bloom's Taxonomy and Keller's ARCS Model to create engaging and compelling learning experiences. This is particularly relevant for teaching entrepreneurship in translation and interpreting studies, which requires both theoretical knowledge and practical application. Entrepreneurship education is vital in this field, as it empowers graduates to adapt to rapid industry changes and pursue opportunities beyond traditional roles. Keller's ARCS Model (Attention, Relevance, Confidence, Satisfaction) is a widely used motivational model in online and blended learning to address learner motivation. Bloom's Taxonomy is a long-standing framework that classifies ways of thinking and is used for curriculum design and assessment. The proposed framework integrates these two models to align motivational strategies with cognitive development. It guides learning outcomes through a step-by-step path, fostering deeper cognitive engagement with entrepreneurial ideas (Bloom's levels) while supporting sustained motivation (ARCS). This comprehensive approach equips students to acquire essential entrepreneurial skills and remain engaged, preparing them for success as entrepreneurial translators.

Contemporary education increasingly emphasizes the development of higher-order thinking skills, particularly critical thinking, which plays a crucial role in enabling students to analyze information, evaluate evidence, and solve scientific problems. Although STEM-based learning has been widely recognized as an effective approach to promoting inquiry and problem-solving skills, its implementation at the elementary level often remains conventional and rarely integrates digital technologies systematically. This limitation creates a significant knowledge gap regarding how digitally integrated STEM learning environments can support students' critical thinking in science education. This study aimed to examine the effect of integrating a digital STEM approach on elementary students' critical thinking skills in science learning. A quasi-experimental design with a non-equivalent control group was employed involving 60 fifth-grade students from an elementary school in Indonesia. Data were collected through a rubric-based critical thinking test, classroom observation sheets, and a teacher perception questionnaire, and analyzed using descriptive statistics and t-tests. The results revealed that the experimental group demonstrated significantly greater improvements in critical thinking than the control group (t = 4.87, effect size = 0.82), with notable gains across the dimensions of analysis, evaluation, and problem-solving. These findings indicate that digital STEM integration fosters more interactive learning environments that enhance students' cognitive engagement in science learning. The study contributes theoretically by strengthening the linkage between STEM Education Theory and Critical Thinking Theory. It provides practical implications for the design of technology-supported science instruction in elementary education.

ABSTRACT This study proposes and validates a new model termed Exploratory Talk-Driven Collaborative Learning (ETDCL), integrating structured exploratory talk into Collaborative Learning (CL) to enhance translation problem-solving behaviours. A 16-week quasi-experimental study compared an experimental group (EG) using ETDCL with a control group (CG) following traditional CL. Multimodal data were analysed through a mixed-methods approach. Key findings include: (1) Although both groups displayed similar proportions of uninterrupted problem-solving bundles, the EG exhibited a notable increase in the proportion of Collaborative Problem Recognition (CPR) behaviours, reflecting a greater proportional emphasis on problem identification within the collaborative process. (2) The EG showed enhanced individual problem-solving behaviours, characterised by a higher proportion of uninterrupted bundles and a hierarchical distribution of sub-behaviours: Problem Recognition (PR) >Solution Proposal (SP) >Solution Evaluation (SE). Notably, while the EG demonstrated significant within-group reductions in macro-level, syntactic, and lexical errors, its statistically superior performance over the CG was uniquely evident in the reduction of macro-level discourse errors and syntactic errors. (3) The EG showed higher levels of behavioural, cognitive and emotional engagement. This research contributes to translation pedagogy by providing preliminary evidence for ETDCL as a promising model for fostering collaborative and individual problem-solving behaviours.

ABSTRACT With the increasing application of virtual robots in contemporary digital society, virtual influencers have appeared and rapidly gained popularity among young generations. To continuously expand social influence, virtual influencers are motivated to produce fashionable, life-sharing, thoughtful content like real human influencers. However, it remains unclear whether virtual influencers will change the interaction patterns of social media users. Consequently, this study utilises secondary data from the Instagram platform to conduct an empirical analysis, exploring the relationship between two types of anthropomorphism (physical and mental) that virtual influencers exhibit in their posts and online user engagement. The results show that mental anthropomorphism is positively connected with emotional and target cognitive engagement, while physical anthropomorphism is positively associated with behavioural engagement. In addition, perceived eeriness significantly mediates the effects of anthropomorphism type on both emotional and target cognitive engagement. The findings provide important managerial insights for designers and researchers of virtual influencers.

Willingness to communicate is an individual difference variable that is argued to predict the likelihood of a person engaging in communication when the opportunity arises. Researchers have investigated the predicates of WTC, and more recently have looked at how it may predict the amount of language that students use when engaged in task-based discussions (Leeming et al., 2024). This engagement in interaction is hypothesized to lead to language learning. While the amount of language produced could be considered to be a measure of behavioral engagement, the quality of language produced is also important. Students may be talking at length, but not actually contributing meaningfully to the discussion. This paper describes an exploratory study that attempted to answer a simple question. When students are highly willing to communicate, do they use a greater complexity of lexis? This complexity of vocabulary was taken to represent cognitive engagement with the task. Students from two universities completed a questionnaire measuring WTC, and then completed an eight-minute quasi-academic discussion task in groups of three. Their output was transcribed and analyzed for linguistic complexity, which was then correlated with their WTC. Results showed that students who were higher in WTC produced language that had greater lexical diversity, but not more sophisticated lexis. This suggests that complexity of vocabulary may be a useful variable to consider when examining the relationship between WTC and task engagement, providing additional information beyond behavioral engagement.