Artificial intelligence-driven green education for sustainable development goals: A review
The intersection of environmental education and artificial intelligence signifies a paradigmatical paradigm shift towards attainment of the United Nations Sustainable Development Goals, and specifically in SDG 4 (Quality Education) and SDG 13 (Climate Action). This is despite the fact that despite considerable growths in technology, the traditional methods used to dispense education are challenged by the need to be able to offer scalable, personalized and beneficial green education that will communicate environm…
AI methods enable personalized sustainability courses and tools like adaptive learning systems and carbon footprint calculators for environmental literacy.
Effectiveness constrained by digital equity gaps, algorithmic bias, privacy concerns, and limited technological infrastructure in developing countries.
Evidence
- Peer-reviewedInternational Journal of Applied Resilience and Sustainability2026-02-19
How should this claim be treated?
Truvace Impact Record TRV-2026-0368, v1: “Artificial intelligence-driven green education for sustainable development goals: A review.” Truvace, 2026-07-20. /record/TRV-2026-0368 (accessed at citation time). sha256 93fae9a911ecb3ac…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0368 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.
ace