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How Capable are AILPs Today?
How Capable are AILPs Today?
Now that you’ve designed an AILP based on current research, let’s consider how future AI capabilities might transform that design in the coming years. First, how close are today’s AI systems to becoming true agentic partners in learning?
The OECD AI Capability Indicators offer a simple five-level framework for understanding AI progress, from basic rule-following systems to the possibility of full human-equivalent intelligence (OECD, 2025).
Our AI capabilities in 2026 are at approximately level 3. As you move up each level, AI becomes more adaptive and autonomous: in other words, more human-like. Whether this raises excitement or concern depends on your perspective, but either way, it certainly raises many questions.
The AI Capability Indicators are based on nine scales that include:
language
social interaction
problem-solving
creativity
metacognition
memory
vision
manipulation
and robotic intelligence.
Click here to explore these scales more deeply and discover what they look like from levels one to five. We recommend selecting three scales that you're personally interested in rather than trying to digest all of the scales.
The 4 Pillars of AILP Capabilities
The 4 Pillars of AILP Capabilities
The capability scales translate directly into the four pillars of AILP capabilities, discussed below. As you explore the following information, consider: which capability pillar is your design most and least adept with?
Personalized Tutoring
Goal:
To create learning experiences that adjust to each learner’s needs, pace, background knowledge, and way of thinking.
Focus:
Instead of delivering identical content to every student, AILPs can respond in real time, asking guiding questions, adjusting difficulty, offering practice when needed, and changing how concepts are explained.
Socio-Emotional Engagement
Goal:
To support not just what people learn, but how they experience learning, adapting to their mood and communicating in ways they will respond to.
Focus:
Learning is not purely cognitive. It’s also social, emotional, and deeply human. This pillar focuses on AI’s growing ability to encourage motivation, adapt communication styles, support reflection, and create meaningful interactions.
Content Synthesis and Research
Goal:
To expand how educational knowledge is gathered, organized, created, and explored, with AI acting as more than just an information retrieval tool.
Focus:
This pillar examines AI’s shift from summarizing existing information to becoming an active research and thinking partner. It focuses on organizing content, generating summaries, and helping learners make sense of complex material.
Educational System Integration
Goal:
To weave AI into the broader educational system, from classrooms and institutions to policy and access, changing how education works at every level.
Focus:
What happens when AI becomes part of the infrastructure of education? This pillar includes supporting teachers, streamlining institutional processes, improving accessibility, and helping schools respond to changing learner needs.
Exploring What's Next
Exploring What's Next
Below is a tool for exploring the capabilities of AILPs in three timeframes:
current
short-term predictions (1-5 years)
and long-term predictions (5-15 years).
As AI capabilities evolve, they may reshape teaching, learning, and educational systems in different ways. Explore the four pillars, choosing one or two to focus on. There is a lot of information, so don't feel as though you need to explore every nook and cranny.
Consider: Which pillars are most applicable to your work, interests, or the AILP you designed? Which specific affordances are most impactful? Be selective and intentional with your choices!
The information included and linked in this page was sourced from: OECD, 2025a; OECD, 2025b; OECD, 2025c; OECD, 2026; Stanford, 2026; UNESCO, 2023. Information was amalgamated by NotebookLM, then verified by the author.
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