AI Footprints Estimator for Students

How Does This App Connect to UNESCO & OECD Guidance & Competencies?

Reference Documents:

Some references below refer to the "technical report", which is here: https://sjtylr.net/2026/01/29/ai-impact-estimator-2026/

UNESCO Guidance on GenAI in Education & Research (2023)

UNESCO calls for GenAI use to be intentional, proportionate, and educationally justified, not default or convenience-driven.
The estimator requires users to declare purpose via task type (text, coding, research, image, video), making AI use an explicit choice rather than an invisible background process.
Scenario comparison (task choice × grid × model efficiency) makes “Should I use AI here?” a defensible judgement, not a moral rule.

The technical report documents assumptions, calculation logic, and known exclusions, modelling transparent explanation of AI system impacts.
Expandable methodology sections allow learners to interrogate how results are generated and where uncertainty remains.
Students experience transparency as an ethical practice, not a hidden technical feature.

Weekly, monthly, and semester projections make accumulated AI impact over time visible.
Task breakdowns reveal how repeated low-impact actions can scale into significant environmental costs.
Learners are encouraged to think longitudinally about AI use rather than focusing on isolated interactions.

Environmental sustainability is treated as a core ethical dimension of AI use, alongside accuracy, bias, or privacy.
The tool expands ethical reasoning beyond immediate classroom concerns to include global and intergenerational impacts.

UNESCO AI Competencies for Students (2024)

The estimator frames AI as a socio-technical system shaped by human decisions, infrastructure, and context.
Students must actively choose inputs, reinforcing that AI outcomes are contingent, not inevitable.
The absence of a single “correct” footprint value promotes reflective judgement over compliance.

Environmental impacts (energy, carbon, water) are made visible as ethical considerations in everyday AI use.
Students compare trade-offs between AI-enabled and non-AI approaches, supporting nuanced ethical reasoning.
Sustainability is embedded as part of responsible AI engagement rather than an optional extension.

Model efficiency tiers illustrate that different AI systems have radically different environmental profiles.
Task-based inputs demonstrate that not all AI uses are equivalent in impact.
Students develop a more accurate mental model of how AI systems operate at scale.

The report explicitly foregrounds uncertainty, variability, and missing data.
Learners are positioned as critical interpreters who must qualify claims and question assumptions.
Students practice distinguishing order-of-magnitude estimates from precise measurements.

UNESCO AI Competencies for Teachers (2024)

The tool supports teachers in modelling responsible AI decision-making in real classroom contexts.
Educators can justify AI use based on learning value and environmental cost, not convenience alone.
It enables a shift from rule-based AI policies to principled professional judgement.

The estimator supports inquiry, systems thinking, and interpretation rather than automating conclusions.
Teachers can design learning experiences where AI deepens understanding rather than replacing thinking.
The tool integrates naturally into conceptual, interdisciplinary learning designs.

Explicit limitations model how educators should communicate evolving and incomplete AI knowledge.
Teachers are supported in guiding students through ambiguity rather than presenting false certainty.
The technical report is one example of reflective, evidence-informed teaching practice in emerging AI contexts.

OECD AI Literacies for Students

The OECD AI Literacies emphasise the importance of scenario-based teaching with and around AI.

The estimator connects user behaviour to infrastructure-level consequences.
Students explore relationships between AI usage, energy demand, emissions, and water stress.
AI is framed as embedded within wider environmental and economic systems.

Learners interpret results through multiple lenses rather than accepting outputs at face value.
Equivalencies and projections prompt discussion about representation, scale, and framing.
Students could evaluate how assumptions shape conclusions.

The tool enables learners to make informed choices about when and how to use AI.
Students can identify opportunities to reduce unnecessary AI use without rejecting AI altogether.
Agency is developed through comparison, reflection, and justification.

UNESCO AI Competencies for Students & Teachers with OECD Draft (SJTYLR)

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