Identifying AI Features That Foster Responsible Sustainability Awareness in Children

Abstract: This paper summarizes a literature review-based investigation that combines Systematic Literature Review (SLR) and Grounded Theory (GT) to identify and assess AI features that can help children become more aware of sustainability challenges and act responsibly in ways that promote a greener and more sustainable planet. Given the rapidly expanding integration of artificial intelligence in education, human–technology interaction, cyber-physical environments, and smart home ecosystems, this research questions which and how AI is most effective for encouraging responsible behavior, cultivating curiosity about environmental challenges, and strengthening children's understanding of wider societal issues such as sustainability and climate action. This research aligns with contemporary perspectives in Kids-AI Interaction and ongoing discussions on the fact that AI should not merely automate educational tasks but rather scaffold reflective decision-making, personal responsibility, and civic participation.We conducted systematic analysis of academic publications between 2015–2025 o various AI tools and features including machine learning and LLM/Gen AI, as well the transformative role of these AI in our fields of interest like human–computer interaction, cyber-physical systems, user experience design, and educational technology. Selected articles were analyzed using SLR and then Grounded Theory coding procedures, allowing the identification of recurring patterns, pedagogical strategies, and technological elements that influence children's cognitive, emotional, and ethical development. Research Rabbit, Elicit and Atlas, three emerging AI-powered research platforms were used. Research Rabbit, which is an AI-driven citation network visualization tool enabled discovery of interconnected research clusters and identification of seminal works through interactive mapping of scholarly relationships, accelerating snowballing and citation tracking processes beyond traditional database searches. Elicit is an AI research assistant leveraging large language models was usefull to automatically extract key findings, methodologies, and outcomes from academic papers, enabling rapid synthesis of relevant information across hundreds of studies and intelligent semantic search beyond keyword matching. Finaly, ATLAS.ti which is a a comprehensive qualitative data analysis software was used to automate the time-consuming qualatitative data analytics underlying. systematic Grounded Theory. Atlas was used coding to proposing hierarchical code organization, memo management, network visualization of emergent themes, and collaborative analysis workflows, enabling rigorous theory development from interview transcripts and document collections.Findings indicate that adaptive personalization, context-aware feedback, ethical reasoning modules, and interactive simulations play a central role in encouraging critical thinking, empathy, and responsible decision-making. Cyber-physical interaction models link environmental learning with real-world actions (e.g., smart home energy use or recycling reminders), reinforcing environmentally responsible behaviors. Moreover, data visualization techniques enable children to observe the environmental consequences of their choices in an engaging and meaningful manner, supporting deeper awareness of sustainability. In addition, the research highlights how innovative approaches to services design and interactive systems can create meaningful opportunities for children to learn through exploration and guided autonomy.A major practical outcome of the research is the development of a highly visual, interactive, and game-based Terms and Conditions (T&Cs) interface. Rather than presenting long, text-based consent forms, the prototype transforms T&Cs into a playful digital journey where children actively navigate safety settings, privacy options, and data-sharing explanations. This design draws on principles of novel interaction technologies, user research, and human-centered service innovation, demonstrating how playful, gamified, and multimodal interfaces can enhance transparency, comprehension, and early digital self-regulation.Adopting an expressivist and ethically guided approach, in this research we argue that AI systems designed for children must prioritize transparency, inclusivity, ethical awareness, and responsible participation in digital ecosystems. The goal is not only to support cognitive learning but also to empower young users with the attitudes and behaviors necessary to act responsibly in a technology-rich world. Therefore, the next stage is to embed the best identified AI features into a gamified XR learning to enhance children-AI interaction while making kid more active participants rather than passive consumers of non-sustainable products and services, promoting their ethical usage of AI and its development, environmentally responsibility digital citizenship.

Keywords: Artificial intelligence, children’s education, gamification, digital literacy, responsible behavior, interactive learning

DOI: 10.54941/ahfe1007186

Cite this paper:

​