The term efficacy risks becoming all but meaningless in the AI era.
Walk into any EdTech conference or faculty meeting right now, and you’ll likely hear the word efficacy stuck on repeat. It’s invoked as both a promise and a defense: This tool drives efficacy. That initiative proves efficacy. Our platform ensures efficacy! Beneath the confident language, however, the field doesn’t really have a shared, operational definition.
The crisis of definition stems from the tangle of goals pursued by a range of stakeholders.
- To an investor, efficacy often means adoption rates, low churn, and efficient time-to-mastery metrics that signal market viability of a product and financial return.
- To a school administrator, it might mean graduation rates or aggregate state test score improvements, often viewed through the narrow lens of accountability compliance.
- To a teacher, efficacy is a measure of instructional fit, ease-of-use, and whether a tool genuinely reduces the cognitive load required to differentiate instruction.
This divergence means that when a vendor claims “efficacy,” one person might mean test-score improvement, another engagement, and a third might point to graduation rates. And while these are all potentially true, the assertions are borne out of fundamentally incomplete views of learning.
In this AI-infused moment, that lack of clarity isn’t merely academic. It’s potentially corrosive. If “efficacy” can mean almost anything, then it risks meaning virtually nothing. This ambiguity allows edtech providers to exploit it and carve out the metrics that best showcase their products, which naturally exacerbates the prioritization of short-term gains (like completion percentages) over evidence of durable, long-term learning (like knowledge transfer). Think “grades” vs. “true learning”.
In a marketplace buzzing with AI hype, the absence of rigor around the term throws the door open to invite inflated claims, misaligned expectations, and ultimately, eroded trust within the educational ecosystem. From that, what could follow is a loss of institutional confidence in edtech as a marketplace, a reluctance to invest in powerful tools, and wasted resources on solutions that deliver metrics without delivering meaningful educational progress.
The pressing question, therefore, is this: what should learning efficacy mean in an AI-driven educational landscape? The answer must ultimately be a holistic framework, capturing the true depth and breadth of educational success while moving beyond simple automation and completion rates.
AI Raises the Stakes: Why Measurement Rigor Must Evolve
Measurement rigor in efficacy research has always been a universal requirement, but AI dramatically raises the stakes and introduces new, systemic ways to get fooled. Even well-designed studies, built on traditional methodology, can be undermined by dynamics unique to AI systems. These dynamics create validity threats that must be mitigated through updated research standards:
- Moving target: AI models and prompt chains are often updated and retrained “under the hood” without public announcement. This presents an enormous challenge for longitudinal efficacy studies. Results collected in the first six months of a study may not apply to the intervention used in the last six months, as the system itself has evolved. Therefore, researchers must version or freeze the specific model used for the duration of the study, demanding unprecedented transparency from developers.
- Shortcut learning: AI can supply answers, offer powerful hints, or provide instant scaffolding, making metrics like time-on-task and correctness look robust. However, this success often reflects prompt engineering or immediate retrieval rather than the student’s deep processing or construction of knowledge. This is a classic example of Goodhart’s law (when a measure becomes a target, it ceases to be a good measure), amplified by machine speed. Efficacy studies must employ measures that separate successful task completion from evidence of durable, transferred learning.
- Invisible assistance: In open learning environments (such as homework or take-home exams), it may be impossible to know when a student utilized an external AI tool to complete an assignment. This invisible assistance fundamentally compromises the integrity of traditional diagnostic assessments. Study designs must therefore explicitly distinguish between performance achieved with-AI support and performance achieved without-AI assistance, requiring robust methods for monitoring and authentication.
- Personalization heterogeneity: AI systems thrive on personalization, delivering different learning paths, content, and feedback loops to every single learner. While beneficial, this can also create massive heterogeneity in outcomes. Averages derived from a large cohort can effectively mask who is benefiting and, crucially, who is being left behind or underserved by an algorithm. Because the AI is inherently non-uniform, efficacy studies must prioritize the ethical dimension of personalization, ensuring outcomes are disaggregated by subgroup (e.g., socioeconomic status, prior achievement, language proficiency) to identify and address equity effects.
- Generative error and drift: Generative AI models are fundamentally stochastic and can hallucinate factual content. Furthermore, content injected into the system (via RAG or other mechanisms) can drift or be misinterpreted. These generative errors introduce significant validity threats absent in static instructional materials (like textbooks or pre-recorded lectures). Efficacy models must account for content compliance, factual accuracy, and the risk of the system outputting misinformation.
Before defining a framework, we have to acknowledge these new and amplified threats to validity. Only then can efficacy regain meaningful operational meaning in an environment characterized by constant model updates, invisible interventions, and rapid iteration.
A Non-Negotiable Framework to Efficacy
We must adopt a comprehensive, multi-dimensional definition of efficacy to ensure that educational technology is delivering meaningful value. Below, I define a non-negotiable framework that insists that success cannot be claimed based on a single metric.
1. Engagement with Purpose
AI can churn out endless personalized practice questions or tutoring interactions, fulfilling the basic requirement of keeping a user busy. However, mere activity is not learning. (To paraphrase an old trope, “Is it motion, or is it action?”) Unless those interactions deepen persistence, foster curiosity, and encourage productive struggle – thereby helping learners stay with the hard, effortful work required for deep learning – then engagement is just motion.
The Efficacy Standard: True engagement is measured by evidence of cognitive effort expended on challenging material, successful navigation of complex problem spaces, and a sustained internal motivation to explore topics beyond the minimum required interaction. We must focus on measuring the quality of the struggle, not just the quantity of the screen time.
2. Retention and Transfer
It’s one thing for AI to help a student answer a question today; it’s entirely another for that knowledge to take hold, become integrated into the student’s schema, and be usable in new, unfamiliar contexts next week or next year. Efficacy requires evidence of long-term retention and the crucial skill of transfer of knowledge.
The Efficacy Standard: Success must be validated through delayed post-tests and, most critically, through assessments that require the application of learned concepts to novel problems or integration with other disciplines. If a student can only perform a skill inside the AI environment where they learned it, the efficacy claim is fundamentally weak.
3. Critical Thinking and Judgment
In an AI-rich environment, easy answers are cheap and plentiful. AI tools can rapidly generate summaries, outlines, and initial drafts at the click of a button. Therefore, discernment, evaluation, and judgment have become more valuable.
The Efficacy Standard: Efficacy must include the learner’s ability to weigh conflicting information, judge the veracity and bias of AI-generated content, synthesize disparate sources, and defend a chosen course of action with clear reasoning. We need evidence that the technology is helping students develop meta-cognitive skills – i.e., the ability to think about their own thinking – which are the ultimate defense against superficial learning.
4. Equity of Impact
We can’t claim “efficacy” if the gains are concentrated solely among students who already have the most advantages. AI has the profound potential either to level the playing field or to drastically widen it, particularly if bias is encoded in the training data or the personalization algorithm.
The Efficacy Standard: Efficacy must account for who benefits, not just whether someone benefits. This requires rigorous, disaggregated data analysis to demonstrate that the technology is reducing, or at a minimum not further exacerbating, existing achievement gaps across all demographic subgroups – including economically disadvantaged students, students with disabilities, and English language learners. An equitable impact must be an explicit measure of success.
5. Human Growth Beyond the Screen
AI excels at optimizing for explicit performance metrics, such as test scores, speed, and accuracy. However, education has always been about more than just measurable performance. The “soft” skills necessary for success in life and work – communication, collaboration, resilience, empathy, and civic responsibility – are not mere extras; they are foundational outcomes.
The Efficacy Standard: Any complete efficacy model must include measures of socio-emotional and professional development that transcend the digital interface. This means designing studies that evaluate the capacity of AI tools to facilitate effective teamwork, promote intrinsic resilience in the face of failure, and prepare students for complex human interactions in the workforce. Ignoring these meta-skills renders the efficacy model incomplete by design.
America’s AI Action Plan and the Call for Educational Efficacy
At the same time as we wrestle with these issues of measurement and efficacy, and as crucial context, the U.S. Government has laid out an action plan – “Winning the AI Race: America’s AI Action Plan” – which lays out more than 90 federal policy actions tied to three core pillars: accelerating innovation, building AI infrastructure, and leading in international diplomacy and security.
The plan also outlines specific educational priorities, recognizing that preparing the future workforce requires a fundamental shift in instructional approach:
- Expanding AI education for American youth by building foundational AI literacy and critical thinking skills in K-12 and beyond. This initiative focuses on demystifying AI and ensuring students understand how these systems work, what their limitations are, and how they impact society.
- Giving professional development to educators so they can responsibly and effectively integrate AI tools into their pedagogical practice, moving beyond simple task automation toward complex instructional design.
- Using grant funding and public-private partnerships to develop high-quality instructional tools that harness AI to support career and college pathway exploration, advising, and navigation. This is a direct policy mandate for tools to demonstrate verifiable, long-term impact on student trajectory.
These initiatives directly intersect with the question of efficacy. When policy emphasizes career readiness, pathway navigation, responsible AI integration, and the cultivation of advanced cognitive skills, it implicitly demands that educational tools demonstrate measurable, equitable learning outcomes – not just engagement metrics or user satisfaction. The policy environment elevates efficacy from a commercial claim to a national priority.
For publishers, EdTech companies, and institutions, the message is clear: efficacy can no longer be an internal marketing term used to sway purchasing committees. It must be a transparent, externally validated measure of how learning products drive verifiable progress toward educational, workforce, and equity goals. The failure to adopt robust, common standards risks attracting regulatory scrutiny and slowing the necessary adoption of truly transformative technologies.
Defining the New Path: Transparent, Multi-Dimensional, and Accountable
Clearly, efficacy can’t just mean students clicked more or scores went up during the pilot. If we allow the definition to drift toward whatever short-term metric is easiest or cheapest to measure, we risk permanently eroding the trust educators and institutions must place in AI-enhanced learning systems. A low bar for efficacy is not just poor research; it is poor business strategy.
Instead, we need an efficacy framework that is transparent, multi-dimensional, and student-centered—one that captures engagement, retention, higher-order thinking, equity, and human development. This framework must serve as the common language for collaboration between developers, researchers, and administrators.
Such a framework should be:
- Structured: Built on clearly defined dimensions with consistent, standardized measurement approaches that can be replicated and validated across diverse contexts and learner populations.
- Validated: Grounded in longitudinal, rigorous, classroom-based evidence rather than small-scale pilot data or marketing success stories. Validation must involve external, third-party reviewers and transparent methodologies that hold up to peer review, moving efficacy claims from vendor promises to verifiable research findings.
- Inclusive: Ensuring that efficacy is measured also for underprepared learners, working adults, caregivers, and online students. An inclusive framework recognizes that the greatest value of edtech lies in its potential to serve the most marginalized populations.
- Aligned: Integrated with existing policy frameworks like America’s AI Action Plan and broader education and accreditation standards (e.g., alignment with regional or professional accreditation bodies). This ensures that efficacy is not measured in a vacuum but contributes directly to institutional quality assurance and regulatory compliance.
If educators, developers, and policymakers can agree on transparent standards for efficacy – anchored in equity, rigor, and transfer – then “efficacy” will no longer be a hollow buzzword used to justify any marginal increase in performance. It will become the authoritative benchmark by which meaningful educational progress is measured and achieved.
