Journal of Education, Society and Behavioural Science

The Edutainment Revolution: Designing Ethical, Adaptive and Immersive Technology for the Next Generation

R. P. Vansdadiya — 2026-02-21

The fact that the educational goals and digital entertainment mechanics converge increasingly quickly has boosted the rise of edutainment as a new paradigm in modern learning scenarios. This change can be fueled by the learning orientations of the digitally native generations and the growing usage of artificial intelligence (AI), immersive experiences and gamified educational systems in both formal and informal education. The research that will be critically reviewed in this paper will focus on AI-enabled personalisation, metaverse-based immersive learning, and advanced gamification, and, in particular, what they suggest regarding children's learning, development, and well-being. Based on peer-reviewed articles in educational technology, learning sciences, and human-computer interaction, the review summarized the evidence on the pedagogical affordances, including adaptive feedback, experiential learning, and motivational engagement, as well as the ongoing issues of cognitive load, digital equity, data governance, and developmental risk. Instead of the detached treatment of these technologies like autonomous entities, the review unites them under a developmental and ethical approach that effectiveness of edutainment does not lie in technological advances but rather in design concepts that are guided by principles. The article proposes three interdependent design imperatives, Child-Centered Ethics, Participatory Design, and Developmental Human-Computer Interaction as a framework of responsible designing of educational technologies. The review offers a synthesised conceptual base to the current research in the field, as researchers, designers, and policymakers can use emerging technologies as fair and developmentally suitable driving forces of learning instead of fuelling the current educational disparities.

Natural Language Processing in Artificial Intelligence: Techniques, Sentiment Analysis and Practical Applications

Mingxia Zhang — 2026-02-21

With the rapid advancement of artificial intelligence, Natural Language Processing (NLP) has become a pivotal bridge between human communication and machine interpretation. This paper provides a comprehensive investigation into the conceptual framework, technical methodologies, and practical applications of NLP, with a focused examination of text sentiment analysis as a core NLP task. I detail methodological approaches ranging from lexicon-based to deep learning techniques, and demonstrate the implementation of sentiment analysis through a concrete case study using the Baidu artificial intelligence (AI) Open Platform. Experimental results from the case study illustrate the model’s ability to accurately capture overall sentiment, even in texts containing mixed expressions, for instance, correctly identifying a predominantly positive tone in a customer review despite minor criticisms. Furthermore, the study elucidates the substantial value of sentiment analysis across domains such as e-commerce, social media monitoring, and market research, while also addressing persistent challenges including context-dependency, figurative language, and multilingual processing. Finally, I outline emerging research directions aimed at enhancing the robustness, explainability, and applicability of sentiment analysis in real-world scenarios.

Analyzing Differences in Problem-Solving Ability Across Levels of Assertive Behaviour among Female Secondary School Students

Manjari Tewari — 2026-02-21

Assertive behavior is defined as the healthy expression of emotions, thoughts, and wants in a direct, honest, and socially acceptable manner. The present study examined differences in problem-solving ability across levels of assertive behaviour among female secondary school students. For this purpose, 258 female students aged 14 to 18 years were randomly selected from government, government-aided and private secondary schools (Grades 9 to 12) across various blocks of Almora district of Uttarakhand state. To gather data, Assertive Behaviour Assessment Scale (ABAS) developed and standardized by Dhondhiyal & Gangwar (2012) and Problem-Solving Ability Test (PSAT-D) constructed and standardized by Dubey & Mathur (2006) were administered. Data were analyzed by calculating the mean, standard deviation, one-way ANOVA and post-hoc comparisons using the Bonferroni correction in SPSS. The results of one-way ANOVA revealed a statistically significant difference among the groups, F(2, 255) = 3.55, p = 0.03, with a small effect size (η² = 0.027). The post-hoc comparisons indicated significant difference on the problem-solving ability among female students with 'high' and 'low' assertive behaviour. Although, the effect size was modest, the findings suggest that assertive behaviour is associated with differences in problem-solving ability among female secondary students. The study highlights the need for educational interventions such as assertiveness training, counseling, and communication-skills development to support students' problem-solving ability.