Climate literacy is crucial to increasing public understanding and engagement with the global climate catastrophe. However, current climate education approaches often fail to effectively raise concern and action, particularly across diverse age groups. This study makes a modest attempt to detail the design and development of a novel multilevel interactive digital climate education platform for early learners, adolescents, and adults using adaptive learning pathways, personalized content delivery, multimedia interactivity, and gamification features to promote learner engagement as well as learning outcomes across different age levels. A mixed-methods research design was used involving pre and post-survey quantitative measures as well as qualitative user experience testing. Post-survey results indicated significant improvement in climate literacy knowledge, attitudes towards the environment, and self-efficacy beliefs regarding individual efforts to mitigate future climate impacts (response efficacy), regardless of learner age group. The comparative analysis thus revealed certain content preferences by age as well as interaction patterns among functionalities and learning gains between groups based on user perspectives that point towards differentiated preference areas linked with diverse ages. The resulting platform exemplifies interactive digital technologies’ potential for achieving sustainable behavior change through optimised synergies with large-scale educational interventions for inducing positive spillover effects in terms of broader widespread climate change engagement impact over generational transition pragma.

The numbers of educational institutions are growing at par with the lost student rate in a country like India. When a missing student is found we need to identify the student on the strength of some common parameter like student name, his/her institution name, branch or class etc. But we never get accurate and complete information in most of the cases to identify or recognize a lost student. In such a situation, a soft computing model can be a striking choice to track a lost student on the basis of partial information. In the past we propose soft computing model for the same. This paper proposes a more optimized parallel soft computing model which takes half of the time taken by the earlier single thread model for identifying a lost student on the basis of imprecise and partial information. The system is tested meticulously on a database of 50000 records and an efficiency of 94% is obtained.