How AI and cognitive science can improve learning
When content is rich, learning outcomes become a real differentiator. But evaluation, the one mechanism that most directly shapes outcomes, remains an afterthought. This is not because the team thinks this is ideal. That’s because evaluation infrastructures have evolved around static allocation banks, infrequent testing, and tuning workflows that don’t support continuous adaptation.
Learning science has long shown that assessment in education supports learning most effectively when it actively shapes practice, guiding what needs to be revisited, how difficulty progresses, and when learners are ready to move on. research evidence [1] show that repeated low-stakes retrieval practice significantly improves long-term retention and transfer of learning, positioning assessment itself as a driver of learning rather than just a measurement tool.
Until now, building such systems in production has been expensive and complex, as adaptive sequencing, persistent learner models, and frequent low-stakes evaluations require extensive manual effort. AI puts this into practice by dynamically generating questions, updating learner models, and enabling continuous, low-overhead assessment at scale. Despite these technological advances, most platforms still have not implemented tightly integrated AI-driven assessment in education into daily practice. In this article, we explore the learning gains uncovered by cognitive science and the concrete opportunities this creates for learning platforms over the next few years.
How AI will change assessment in education: 3 key values
1. Efficiency: Scalability and automation
AI reduces the time that experts spend on mechanical tasks. In practice, humans generate a large number of tailored assessment items, suggest options across difficulty levels, draft rubrics, and handle the initial assessment, while remaining responsible for validation and edge cases. To make this more concrete, here are the most common assessment workflows for teams to leverage to get started.
Generate question options and distractions. Creating rubrics and scoring guides. Primary scoring for open-ended responses (with human review for ambiguous cases) Tag items by concept and level of difficulty, including common misconception patterns.
This is not a hypothesis. Leading assessment providers are already operating hybrid scoring models at scale. This takes time away from manual tasks such as building item banks, adjusting formats, and reviewing results. Instead, teams can focus on improving curriculum design, instructional quality, and learner outcomes with a clearer, faster feedback loop from learner performance to program decisions.
2. Effectiveness: Supports real learning, not formal learning
The barrier is always execution. You need to decide what to see next, adjust the task, and provide feedback that is specific enough for learners to take action. AI makes it easy to operationalize these learning science patterns within real products. As assessment in education becomes adaptive and formative, several features emerge repeatedly.
Adaptive complexity (adjusts difficulty based on performance) Dynamic selection of task formats (MCQs, short answers, scenarios) Frequent low stakes checks to facilitate search and reduce ‘exam cliffs’. A personalized remediation path to mastery. Space logic to recheck knowledge after time has passed.
Static testing vs. AI-driven formative assessment (a quick comparison):
Static test: “One Quiz → Score → Next”.
AI-driven assessment: “Frequent acquisition checks → targeted feedback → next best task selection → mastery tracking.”
systematic review [2] We also found that AI-enabled adaptive platforms adjust content and learning paths based on learner performance, supporting continuous feedback loops rather than one-time assessments.
3. Insights: Detailed analysis of knowledge and progress
Traditional assessment analysis answers a narrow question: “Did I pass?” For professional learning, corporate training, or certifications, where the buyer values preparation and the learner’s confidence to transfer to real-world tasks, this is rarely enough.
AI-driven assessment enables richer signals such as error patterns, time to recall, hint dependencies, and retention delays. These signals support early identification of conceptual gaps and risks of learning deficits, while also grounding claims of readiness and skills more defensively. Assessment moves from a single measurement event to a layer of intelligence that informs learning, progress, and decision-making.
What this change will enable: As learning products move from selling content to selling outcomes, assessment becomes central to value creation. Platforms that treat assessment as core infrastructure, rather than an add-on to reporting capabilities, enable stronger retention, clearer differentiation, and new product dimensions built around measurable learning outcomes.
What will become of the major platforms: strategic opportunities
As AI-driven assessment becomes operational at scale, the real question for learning platforms is not whether to use AI, but where it will be most effective. Moving forward, the platform will do more than just add AI capabilities to existing courses. They will rethink how skills are defined, how learning is adapted, and how outcomes are measured.
Competency maps tailored to cognitive science
Most current competency frameworks are static checklists that mark whether the learner has seen the content, rather than whether the learner remembers or can apply it. The future is a dynamic competency map that reflects both proficiency and knowledge evolution.
Competencies become measurable and defensible rather than descriptive. AI can incorporate learning science patterns into readiness modeling. Platforms can connect learner behavior to predictive metrics rather than binary pass/fail.
Evaluation as an infrastructure layer
Assessment is often treated as an “internal” feature of the course. The next wave will incorporate this as an infrastructure service. It is ongoing, invisible, and fundamental. Platforms can provide readiness scores, skill validation APIs, and microcredentials along with completion badges. Companies can purchase analytics dashboards tied to actual learning outcomes and content engagement. Credentialing systems can support ongoing evidence of proficiency and exam snapshots.
How to build AI ratings without reworking the platform
Many teams are hesitant to tackle AI evaluation because they imagine a large-scale rewrite. The good news is that you can start adding intelligence gradually.
Block 1: Human-AI content loop
At the core of any practical AI assessment architecture is a feedback loop in which the AI takes on the routine generative tasks and humans maintain judgment regarding quality and alignment with learning objectives. This “co-creation” approach allows you to scale production of items quickly while maintaining standards.
Block 2: Explainable, learning science-based feedback
Learners trust feedback when they understand why their answer was wrong and what next steps they can take. Effective feedback helps learners understand [3] Where are they, why are they stuck, and how do they move forward?
Block 3: Pilot → Data → Scale
Start with low-risk automation, introduce adaptability to a limited extent, build analytics to uncover conceptual gaps, and iteratively improve quality using performance data and expert feedback. This is an area where research has shown that a hybrid approach increases consistency and reduces scoring bias.
The window is open, but not for long
AI in learning is no longer a question of “if” but whether it actually creates lasting benefits. A key next step will be those that apply AI to reshape learning itself, including assessment, feedback, and decision-making about what learners should do next.
Large-scale assessment in education is now technically feasible. Learning science has long supported search, spacing, mastery, and formative feedback, and AI allows these approaches to be implemented in real products. For teams still in the consideration phase, several practical recommendations are worth noting.
Prioritize evaluation over content. Pilot low-risk, formative use cases. Design as evidence. Always keep abreast of human information.
The next generation of learning platforms will not be defined by the amount of content they deliver, but by how accurately they can guide, measure, and prove learning. And that change has already begun.
source:
[1] Advances in feedback research in educational psychology: Insights into the determinants of feedback processes and effectiveness.
[2] Artificial intelligence in adaptive education: A systematic review of personalized learning methods.
[3] A practical guide to supporting formative assessment and feedback using generative AI
