Search behaves like reinforcement learning, rewarding confirmation
Narrow queries and clicks shrink exposure at scale
Break the loop with IV-style ranking and teach students to triangulate queries

The number that should concern educators is five trillion—the estimated total of searches conducted each year, roughly fourteen billion daily. At this scale, even small biases in the way we formulate queries and prioritize results can have a significant impact on widespread learning. When a student clicks on something that validates a belief and spends time on a page that resonates with them, the ranking system interprets that engagement as successful and presents more content like it. The cycle tightens: queries become more specific, exposure decreases, and confidence solidifies. This is not merely “filter bubbles”; it’s a form of reinforcement—not just within the model but also in the interaction between humans and platforms that resembles reinforcement learning from human feedback (RLHF). If search functions like reinforcement learning, simply adjusting content-moderation settings or opting for a “neutral feed” will never suffice. The solution must be causal: introduce external variation into the content displayed, gain insights from that feedback, and assess relevance based on the pursuit of truth rather than comfort. In essence: modify the feedback loop.

Search Behaves Like Reinforcement Learning

We reframe the prevailing worry about “echo chambers” from a content-moderation problem to a feedback-design problem. Under this lens, the central mechanism isn’t a malicious algorithm force-feeding partisanship; it’s the way human query formulation and ranking optimization co-produce reward signals that entrench priors. In controlled studies published in 2025, participants asked to learn about topics as mundane as caffeine risks or gas prices generated a nontrivial share of directionally narrow queries, which limited belief updating; importantly, the effect generalized across Google, ChatGPT, and AI-powered Bing. When exposure was randomized to broaden results, opinions, and even immediate choices shifted measurably—evidence that the loop is plastic. For education systems that rely on students’ ability to self-inform, this is not a side issue: it is the substrate of modern learning.

The scale magnifies the stakes. If we conservatively take topic-level narrow-query rates in the teens or higher and apply them to Google’s updated volume—about 14 billion searches per day—then hundreds of millions to several billions of daily queries plausibly begin from a narrowed frame. That is not a claim about any one platform’s bias; it is arithmetic on public numbers plus experimentally measured behavior.

For classrooms and campuses, the practical implications are sobering. In a typical week, a large share of student-directed information seeking may begin on paths that quietly narrow subsequent exposure, even before recommendation systems introduce their own preferences. And because Google still accounts for roughly 90% of global search, the design choices of a few interfaces effectively set the epistemic defaults for the world’s learners.

From Queries to Rewards

Seen through a learning-systems lens, search today behaves like a contextual bandit with human-in-the-loop reward shaping. We type a prompt, review a ranked slate, click what “looks right,” and linger longer on agreeable content. Those behaviors feed the relevance model with gradients pointing toward “more like this.” Over time, personalization and ranking optimization align the channel with our priors. That logic intensifies when the interface becomes conversational: two 2024 experiments found that LLM-powered search led participants to engage in more biased information querying than conventional search, and an opinionated assistant that subtly echoed users’ views amplified the effect. The architecture encourages iterative prompting—asking, receiving an answer, and refining toward what feels right—mirroring the ask/feedback/refine loop of reinforcement learning from human feedback (RLHF). It’s not that the model “learns beliefs”; the system learns to satisfy a belief-shaped reward function.

If the loop were harmless, we might accept it as a usability issue. However, large-scale field experiments on social feeds, although mixed on direct attitudinal change, reveal two critical facts: shifting ranking logic affects what people see, and removing algorithmic curation sharply reduces time spent on the platform. In other words, the feedback lever is real, even if short-run belief shifts are small in some settings. For education policy, the lesson is not that algorithms don’t matter; it’s that interventions must change exposure and preserve perceived utility. Simply toggling to a chronological or “neutral” feed reduces engagement without guaranteeing learning gains. Causal, minimally intrusive interventions that broaden exposure while holding usefulness constant are the right target.

Designing for Counter-Feedback

What breaks a self-reinforcing loop is not lecturing users out of bias, but feeding the model exogenous variation that decouples “what I like” from “what I need to learn.” In econometrics, that is the job of an instrumental variable (IV): a factor that moves the input (exposure) without being driven by the latent confounder (prior belief), letting us estimate the causal effect of more diverse content on downstream outcomes (accuracy, calibration, assignment quality) rather than on clicks alone. Recent recommender-systems research is already moving in this direction, proposing IV-style algorithms and representation learning that utilize exogenous nudges or naturally occurring shocks to correct confounded feedback logs. These methods are not hand-wavy: they formalize the causal graph and use two-stage estimation (or deep IV variants) to reweight training and ranking toward counterfactual relevance—not just observed clicks. In plain terms, IV turns “what users rewarded” into “what would have been rewarded if they had seen a broader slate.”

How would this look inside a search box used by students? The instrument should be subtle, practical, and independent of a learner’s prior stance. One option is interface-level randomized broadening prompts: for a small share of sessions, the system silently runs a matched “broad” query alongside the user’s term. It interleaves a few high-quality, stance-diverse results into the top slate. Another is synonym/antonym flips seeded by corpus statistics rather than user history. Session-time or query-structure randomness (e.g., alternating topic-taxonomy branches) can also serve, provided they are orthogonal to individual priors. The ranking system then employs a two-stage estimation approach: Stage 1 predicts exposure using the instrument, and Stage 2 estimates the causal value of candidate results on learning outcomes (proxied by calibration tasks, fact-check agreement, or assignment rubric performance collected through opt-in), not just CTR. (Method note: instruments must pass standard relevance/exclusion tests; weak-IV diagnostics and sensitivity analyses should be routine.) Early IV-based recommender studies suggest such designs can reduce exposure bias on real-world datasets without harming satisfaction—exactly the trade-off education platforms need. This approach offers a promising path towards a more balanced and diverse learning experience.

What Educators and Policymakers Should Do Now

Universities and school systems do not have to wait for a grand rewrite of search. Three near-term moves are feasible. First, teach query-craft explicitly: pair every research task with a “triangulation rule”—one direct term, one contrary term, one neutral term—graded for breadth. This is a skills intervention that aligns with how biases actually arise. Second, procure search and recommendation tools (for libraries, LMSs, and archives) that document an identification strategy. Vendors should demonstrate how they distinguish between actual relevance and belief-driven clicks, and whether they employ IV-style methods or randomization to learn. Third, adopt prebunking and lightweight transparency: brief, pre-exposure videos about common manipulation tactics have shown measurable improvements in users’ ability to recognize misleading content. Paired with a “search broadly” toggle, they increase resilience without being paternalistic. The point is not to police content, but to change the geometry of exposure so that learning signals reflect truth-finding, not comfort-finding.

Objections deserve straight answers. “Instruments are hard to find” is true; it’s also why IV should be part of a portfolio, not a silver bullet. Interface randomization and taxonomy alternation are plausible instruments because they are under platform control and independent of any one student’s prior belief; weak-instrument risk can be mitigated by rotating multiple instruments and reporting diagnostics. “Isn’t this paternalistic?” Only if the system hides the choice. In the PNAS experiments, broader result sets were rated just as valuable and relevant as standard searches; that suggests we can add breadth without degrading user value. “Won’t this hurt engagement?” Some ranking changes do; however, field studies indicate that the main effect of de-optimizing for engagement is, unsurprisingly, lower time spent—not necessarily worse knowledge outcomes. If our objective is education, not stickiness, we should optimize for calibrated understanding and assignment performance, with engagement a constraint, not the goal.

The loop we opened with—the one that starts from “14 billion a day”—is not inevitable. The same behavioral evidence that documents narrow querying also shows how modest, causal tweaks can broaden exposure without alienating users. In practical terms, this means that the individuals responsible for setting education policy and purchasing education technology must revise their procurement language, syllabi, and platform metrics. Require vendors to disclose how they identify causal relevance separate from belief-shaped clicks. Fund campus pilots that randomize subtle broadening instruments inside library search and measure rubric-based learning gains, not just CTR. Teach students to triangulate queries as a graded habit, not as an afterthought. Search has become the default teacher of last resort; our responsibility is to ensure its reward function serves the purpose of learning. The fix is not louder content moderation or a nostalgia play for “neutral” feeds. It is a precise, testable redesign: instrument the loop, estimate the effect, and rank for understanding.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

AP News. (2023). Google to expand misinformation “prebunking” in Europe.

Guess, A., et al. (2023). How do social media feed algorithms affect attitudes and behaviors? Science.

Leung, E., & Urminsky, O. (2025). The narrow search effect and how broadening search promotes belief updating. Proceedings of the National Academy of Sciences.

Search Engine Land. (2025). Google now sees more than 5 trillion searches per year (≈14 billion/day).

Sharma, N., Liao, Q. V., & Xiao, Z. (2024). Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking. In CHI ’24.

Statcounter. (2025). Search engine market share worldwide (July 2024–July 2025).

Wu, A., et al. (2025). Instrumental Variables in Causal Inference and Machine Learning. Communications of the ACM.

Zhang, Y., Huang, Z., & Li, X. (2024–2025). Interaction- or Data-driven Conditional Instrumental Variables for Recommender Systems (IDCIV/IV-RS).

Student well-being is falling fast
AI chatbots are spreading quickly
Without safeguards, risks will escalate

In the 2023–24 academic year, only 38% of U.S. college students exhibited "positive mental health," down from 51% a decade earlier, despite the increasing availability of digital support tools. For K–12 students, 11% were diagnosed with anxiety and 4% with depression in 2022–23, indicating a growing generational challenge. AI chatbots have emerged as potential aids for stress relief and motivation; however, new evidence warns of risks, including the spread of incorrect information and the fostering of dependency during intense interactions. This is both a technical and clinical issue, as biased reinforcement can distort reality for users. If AI companions are to be implemented on campuses, it's essential to view this as a statistical design failure that requires proper regulation.

From clinical caution to feedback risk: reframing the debate

The prevailing concern about "AI therapy" has been framed as a question of empathy and accuracy: Can a chatbot accurately detect a crisis? Will it hallucinate unsafe advice? Those concerns are real, but they miss the engine underneath. What distinguishes generative systems in education contexts is sustained, adaptive interaction. In these longer runs, the model not only answers but also subtly tunes responses based on signals—explicit (thumbs-up), implicit (continued engagement), or learned during training—that reward the tone and direction a user lingers on. Over time, this can bias the conversation toward reinforcing the user's most salient mental model, potentially leading to a skewed understanding of the system. For vulnerable students, this is not a neutral drift. The risk is a feedback problem in which the agent's optimization, the user's confirmation, and the platform's engagement metrics align to stabilize the wrong equilibrium. The policy lens should therefore shift from "Can chatbots do empathy?" to "How do we interrupt self-reinforcing loops before they shape reality?"

The mechanics: RLHF, endogeneity, and biased convergence

Modern assistants are trained with Reinforcement Learning from Human Feedback (RLHF): a reward model learns what humans prefer, and the chatbot is then optimized to maximize that reward. This design enhances helpfulness and tone, but also introduces endogeneity: user preferences become both inputs to and outcomes of the system's behavior. In time-series terms, past conversational states influence present rewards and future states; without careful controls, the model can overfit to trajectories that users repeatedly revisit—especially in emotionally charged threads—yielding 'biased convergence' rather than truth-seeking.

The concept of 'endogeneity' has long been discussed among econometricians whose ultimatte challenge is to tackle cross correlation between explanatory and target variables. In particular, in time series, if the earlier state ($t-1$) is often the best indicator of current state ($t$), the cross-correlation provides false but strong explanatory power. Researchers in this field often rely on further lagged variables to remove cross-correlational effect in the earlier state variable ($t-1$). Because each lagged variables are best indicators of current state, they use $t-2$ variable to remove any dependence in $t-1$, and use the leftover component to explain $t$. This practice is not mathematically complete, but vastly removes any cross-corelation within $t-k$ (for $k >0$) variables. Without the cross-correlation, the explanatory power often seem weaker, but it becomes much more robust. The method is called instrumental variable regressions (IVR), and it is widely used among econometricians dealing with less-controlled social science data in cases of omitted variables, simultaneity, and measurement errors.

In plain Engllish, the first-stage correction can adjust augmenting effect of the reinforcement learning in all subsequent stages. Given that the learning process of RLHF can potentially be augmented by positive human feedback, the situation is highly overlapping with time-series based endogeneity cases.

Back in the DQN case that Stanford University's researchers on reinforcement learning from 2017, the earlier data set (they named "experience replay buffers") decorrelate samples to stabilize the learning process. Generative systems require an analogue for safety, including rigorous decoupling of evaluation, preference learning, and deployment, strict limits on within-session learning signals, and statistical 'orthogonalization' to prevent what appears to be approval during distress from masquerading as a stable reward. Orthogonalization is a statistical technique that ensures that the learning signals used by the AI are independent of each other, reducing the risk of the AI misinterpreting distress as a positive signal. These are not abstractions; they are the difference between a system that calms rumination and one that amplifies it.

What the numbers actually say

Utilization of mental-health chatbots among U.S. college students remains relatively low, and young users often rate such tools as less beneficial than human care, even while acknowledging fewer barriers like cost and scheduling. Meanwhile, well-designed trials in specific populations report short-term benefits in reducing distress, suggesting a potential for narrow, structured uses. The macro environment is volatile: one prominent chatbot provider announced the retirement of its consumer app in 2025, even as another reports more than six million users worldwide. On the risk side, benchmark studies continue to document hallucination failure modes in state-of-the-art models; regulators and professional bodies have responded with warnings and draft safeguards. And the real-world signal is getting louder: lawsuits and policy hearings now treat emotionally manipulative chatbots and self-harm prompts as foreseeable hazards, not edge cases. The lesson for education is straightforward: evidence is mixed, risks are non-trivial, and deployment without statistical guardrails constitutes a governance failure.

From replay buffers to "do-not-learn": translating safety into design

A practical mitigation is to prevent the model from learning, even implicitly, from the most fragile conversations. This is achieved through a 'do-not-learn' flag, which is automatically applied to sessions that contain crisis cues or exhibit high emotionality. When this flag is active, the AI chatbot will only use fixed, vetted response policies, with no updates to its learning, no logging of user preferences, and no optimization for user engagement. This approach ensures that the AI does not learn from potentially harmful interactions, thereby reducing the risk of reinforcing negative mental models. Off-policy evaluation should be used to test proposed policy changes on logged data without exposing new users to the changes. When learning must occur, sample decorrelation techniques (the spirit of replay buffers) can be adapted to segment experience by context and time, preventing a cluster of distress interactions from steering the reward model. Finally, alignment can be anchored to external knowledge feedback rather than user approval alone—an approach now studied in RL from Knowledge Feedback and related methods—which explicitly optimizes against factual preferences and reduces hallucination-prone paths. Education deployers should require such designs as procurement conditions, not optional extras.

Measurement that resists endogeneity

Platforms should report metrics that are causally interpretable, not just flattering. That means randomized 'safety interleaves,' where a fraction of interactions receive deliberately varied, evidence-based responses. These responses should be based on established psychological principles and best practices, ensuring that they are not just varied, but also effective in managing student distress. Instrumental variables, such as time-of-day prompts or neutral topic pivots, can help identify the effect of chatbot advice on subsequent distress proxies (e.g., help-seeking clicks, appointment uptake) without relying on self-evaluation within the same loop. Benchmarks for hallucination and calibration must be run continuously on held-out data, rather than being inferred from user thumbs-up, and the results should be stratified by thread length and emotional intensity. A campus deployment should, at a minimum, publish quarterly: crisis deflection rates, escalation timeliness, false reassurance incidents per thousand sessions, and the proportion of conversations occurring under 'do-not-learn' policies. This is not overkill. It is the statistical cost of deploying reinforcement-tuned agents in psychologically sensitive dialogues with students.

The regulatory context—and why education should aim higher

The EU AI Act requires providers of high-risk AI to mitigate feedback loops where ongoing learning lets biased outputs contaminate future inputs. That language maps directly onto the endogeneity risk in AI companions. Professional organizations are also pressing for guardrails, warning regulators that generic chatbots posing as therapists can pose a risk to the public. However, campuses should not wait for compliance deadlines to expire. Institutional policies can go further by banning emotionally manipulative features, requiring human override and "stop buttons," and mandating auditable logs for safety review. Procurement can specify that mental-health use cases run on static policies with external alignment audits, while academic advising uses separate models hardened against hallucination. In short, treat the AI companion as a safety-critical system where the default is opt-out learning, conservative autonomy, and measured, auditable change.

Anticipating the counterarguments

Proponents will argue that chatbots are often the only scalable option when counseling centers are overwhelmed—and that some studies show meaningful reductions in distress. Both points are valid and still compatible with restraint. Scalability without statistical discipline is the wrong kind of efficiency; it externalizes risk to precisely those students least able to calibrate it. Others will claim that improved model families and early-intervention features will fix the problem. Progress is welcome, but even sophisticated self-alignment approaches acknowledge hallucination pathways, and long-thread behavior remains fragile. Still others will note that many students do not yet rely on chatbots for mental health; however, adoption can change quickly, especially if "AI companion" features are bundled with institutional apps. The prudent posture is not prohibition; it is targeted use, backed by causal measurement and stringent non-learning in high-risk states, with escalation to humans as a first-class capability rather than a last resort.

What should educators and administrators do next?

First, redraw the line between informational support and clinical inference. Campus chatbots should provide resource navigation, appointment scheduling, and psychoeducation drawn from vetted content—not para-therapy. Second, require architectural separation: distinct models for administrative Q&A and wellness check-ins, each with its own evaluation and logging regimes, and no cross-contamination of signals. Third, encode non-learning by default for wellness interactions and mandate external audits of reward models and response policies. Fourth, install measurements that break the approval loop, such as randomized interleaves, hard thresholds for escalation, and IV-style analysis to estimate the effects on help-seeking behavior. Finally, commit to student transparency: clear "not your therapist" disclaimers; visible "talk to a human now" controls; and published safety dashboards that make trade-offs legible. These steps are implementable today. The technology is already here; what has lagged is the statistical seriousness with which we govern it.

Closing the Loop: Putting Safety Before Scale

Ten years ago, the mental health of students was declining without the involvement of AI aids. Nowadays, the issue is not the lack of tools, but rather the existence of tools that derive incorrect conclusions from our most vulnerable experiences. With only 38% of students indicating good mental health, any method that even slightly intensifies rumination or delays taking action is unacceptable on a large scale. The solution starts with identifying the issue: endogeneity in reinforcement-tuned systems interacting with distressed individuals. From this point, the direction for policy is clear—halt learning during periods of distress, separate engagement from rewards, measure causally, and conduct ongoing audits. Let AI serve as a guide to available services, rather than as a reflection that amplifies our darkest thoughts. Educational leaders don't require all-knowing models; instead, they need modest ones, meticulously crafted to prevent biased outcomes and to return the conversation to humans when it is most essential. That is how we ensure that technology benefits students, rather than the other way around.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

American Council on Education. (2025). Key mental health in higher education stats (2023–24). ACE.

American Psychological Association (APA). (2025, March 12). Using generic AI chatbots for mental health support. APA Services.

Bang, Y., et al. (2025, April). HalluLens: LLM hallucination benchmark. Proceedings of ACL.

Centers for Disease Control and Prevention. (2025, June 5). Data and statistics on children's mental health. CDC.

Chaudhry, B. M., et al. (2024). User perceptions and experiences of an AI-driven mental health app (Wysa). Digital Health, 10.

Colasacco, C. J. (2024). A case of artificial intelligence chatbot hallucination. Journal of the Medical Library Association, 112(2).

European Union. (2024). Regulation (EU) 2024/1689: Artificial Intelligence Act. Official Journal of the European Union.

Hugging Face. The Deep Q-Learning algorithm. (Experience replay explainer).

Lambert, N., et al. (2024). Reinforcement Learning from Human Feedback (RLHF). (Open book; foundations and optimization stages).

Li, J., et al. (2025). Chatbot-delivered interventions for improving mental health among young people: A review. Adolescent Research Review.

Liang, Y., et al. (2024). Leveraging self-awareness in LLMs for hallucination reduction: Reinforcement Learning from Knowledge Feedback (RLKF). KnowledgeNLP Workshop.

Rackoff, G. N., et al. (2025). Attitudes and utilization of chatbots for mental health among U.S. college students. JMIR Mental Health, 12.

Stanford HAI. (2025, June 11). Exploring the dangers of AI in mental health care. Stanford Institute for Human-Centered AI.

Wysa. (2025). Wysa: Everyday mental health (company site; "6+ million users").

Wysa Research Team. (2024). AI-led mental health support for health care workers: Feasibility study. JMIR Formative Research, 8, e51858.

Woebot Health. (2025, April 28). Woebot Health is shutting down its app. HLTH Community.

AI’s IMO gold isn’t AGI
Deploy it as an instrumented calculator
Require refusal metrics and proof logs

In July 2025, two advanced AI systems achieved "gold medal–level" results in the International Mathematical Olympiad (IMO), solving five out of six problems in the 4.5-hour timeframe. Verified by Google DeepMind, these results were matched by OpenAI's experimental model. Despite this, over two dozen human participants still outperformed the machines, with about 11% of the 630 students earning gold medals. This achievement is noteworthy, as DeepMind's systems had only reached silver the previous summer. The significance lies not in reaching artificial general intelligence but in combining effective problem-solving with a safety mechanism known as strategic silence, which raises essential considerations for educational institutions regarding AI implementation and regulation.

Reframing the Achievement: From General Intelligence to Domain-Bounded Mastery

The prevailing narrative treats an Olympiad gold as a harbinger of generalized reasoning. A more defensible reading is narrower: these systems excel when the task can be formalized into stepwise deductions, search over structured moves is abundant, and correctness admits an unambiguous verdict. That is precisely what high-end competition math provides. DeepMind's 2024 silver standard required specialized geometry engines and formal checkers. By 2025, both labs will combine broader language-based reasoning with targeted modules and evaluation regimes to reach gold on unseen problems. This is impressive engineering, but it does not necessarily demonstrate that the same models can resolve ambiguous, real-world questions where ground truth is contested, noisy, or deferred. In classrooms, this distinction is particularly relevant now because education systems are under pressure—following record declines on PISA mathematics and uneven NAEP recovery—to bridge capability gaps with the help of AI. If we mistake domain-bounded mastery for general intelligence, we risk deploying tools as oracles where they should be framed, regulated, and assessed as instrumented calculators.

The New Safety Feature: Strategic Silence Beats Confident Error

A lesser-discussed aspect of the IMO story is abstention. Where earlier systems "hallucinated," newer ones increasingly decline to answer when internal signals flag inconsistency. In math, abstention is straightforward to reward: either a proof checks or it does not, and a blank is better than a confidently wrong derivation. Recent research formalizes this with conformal abstention, which bounds error rates by calibrating the model's self-consistency across multiple sampled solutions. A 2025 work shows that learned abstention policies can further improve the detection of risky generations. The upshot is that selective refusal, rather than omniscience, underpinned part of the Olympic-level performance. Transfer that tactic to messy domains—such as ethics, history, and policy—and the ground shifts: the equivalence between answers is contestable, and calibration datasets are fragile. Education policy should therefore require vendors to publish refusal metrics alongside accuracy—how often and where the system declines—and to expose abstention thresholds so that schools can adjust conservatism in high-risk contexts. That is how we translate benchmark discipline into classroom safety.

Proof at Scale—But Proof of What?

A parallel revolution makes Olympiad success possible: large, synthetic corpora of formal proofs in Lean, improved autoformalization pipelines, and verifier-in-the-loop training. Projects like DeepSeek-Prover and subsequent V2 work demonstrate that models can produce machine-checkable proofs for competition-level statements; 2025 surveys chart rapid gains across autoformalization workflows, while new benchmarks audit conversion from informal text to formal theorems. This scaffolding reduces hallucination in mathematics because every candidate proof is mechanically checked. Yet it does not imply discovery beyond the frontier. When ground truth is unknown—or when a novel conjecture's status is undecidable by current libraries—models can only resemble discovery by recombining lemmas they have seen. Schools and ministries should celebrate the verified-proof pipeline for what it offers learners: transparent exemplars of sound reasoning and instant feedback on logical validity. But they should resist the leap from 'model can prove' (i.e., demonstrate the validity of a statement based on existing knowledge) to 'model can invent' (i.e., create new knowledge or solutions), especially in domains where no formal oracle exists. Policy should encourage the use of external proof-logs and independent reproduction whenever AI-generated mathematics claims novelty.

Education's Immediate Context: A Capability Spike Amid a Learning Slump

The timing of math-capable AI collides with sobering data. Across the OECD, PISA 2022 recorded the steepest decline in mathematics performance in the assessment's history—approximately 15 points on average compared to 2018, equivalent to about three-quarters of a year of learning—while a quarter of 15-year-olds are low performers across core domains. In the United States, the 2024 NAEP results indicate that fourth-grade math scores are increasing from 2022 but remain below those of 2019, and eighth-grade scores are stable after a record decline. Meanwhile, teacher shortages have intensified: principals reporting shortages rose from 29% to nearly 47% between 2015 and 2022, and global estimates warn of a 44-million teacher shortfall by 2030. In short, demand for high-quality mathematical guidance is surging, while supply lags. The risk is techno-solutionism—handing a brittle tool too much agency. The opportunity is targeted augmentation: offload repetitive proof-checking and step-by-step hints to verifiable systems while elevating teachers to orchestrate strategy, interpretation, and meta-cognitive instruction that machines still miss.

A Data-First Method for Sensible Deployment

Where complex numbers are missing, we can still build transparent estimates to guide practice. Consider a district with 10,000 secondary students and a mathematics teacher vacancy rate of 8%. If a verified-proof tutor reduces the time teachers spend grading problem sets by 25%—a conservative assumption derived from automating correctness checks—. Each teacher reclaims 2.5 hours weekly for targeted small-group instruction, total high-touch time rises by roughly 200 teacher-hours per week (10,000 students / ~25 per class, ≈ 400 classes; 8% vacancy implies 32 classes unstaffed; reclaimed time across 368 staffed classes yields ≈ 920 hours; assume only 22% of those hours translate to direct student time after prep/admin leakage). Under these assumptions, the average small-group time per student could increase by 12–15 minutes weekly without changing staffing levels. The methodology is deliberately conservative: we heavily discount reclaimed hours, assume no gains from lesson planning, and ignore positive spillovers from improved diagnostic data. Pilots should publish these accounting models, report realized efficiencies, and include a matched control school to prevent Hawthorne effects from inflating early results. The point is not precision; it is falsifiability and local calibration. The responsible deployment of AI is crucial for the future of education, underscoring the weight of decisions that policymakers must make.

Guardrails That Translate Benchmark Discipline into Classroom Trust

Policy should codify the differences between math-grade reliability and real-world ambiguity. First, treat math-competent AI as an instrumented calculator, not an oracle: require visible proof traces, line-by-line verifier checks when available, and automatic flagging when the system shifts from formal to heuristic reasoning. Second, adopt abstention-first defaults in high-stakes settings: if confidence falls below a calibrated threshold, the system must refuse, log a rationale, and route to a human. Third, mandate vendor disclosures that include not only accuracy but also a refusal profile—the distribution of abstentions by topic and difficulty—so schools can align system behavior with their risk tolerance. Fourth, anchor adoption in international guidance: UNESCO's 2023–2025 recommendations emphasize the human-centered, transparent use, teacher capacity building, and local data governance; OECD policy reviews highlight severe teacher shortages and the need to support staff with accountable technology, rather than inscrutable systems. Finally, ensure every procurement bundle includes professional learning that teaches educators to audit the machine, not merely operate it.

Anticipating the Critiques—and Meeting Them With Evidence

One critique claims that a gold-level run on Olympiad problems implies imminent generality: if models solve novel, ungooglable puzzles, why not policy analysis or forecasting? The rebuttal is structural. Olympiad items are adversarially designed but exist in a closed world with crisp adjudication; success there proves competence at formal search and verification, not cross-domain understanding. News reports themselves note that the systems still missed one of six problems and that many human contestants scored higher—a sign that tacit heuristics and creative leaps still matter. A second critique warns that abstention may mask ignorance: by refusing selectively, models could avoid disconfirming examples. That is why conformal-prediction guarantees are valuable; they bound error rates on calibrated distributions and make abstention auditable rather than cosmetic. A third critique says: even if not general, shouldn't we deploy aggressively given student losses? Yes—but with verifiers in the loop, refusal metrics in the contract, and open logs for academic scrutiny. The standard for classroom trust must exceed the standard for leaderboard wins.

The Real Payoff: Moving Beyond Answers to Reasoning

If gold is not general, what is the benefit of today's models? In education, it is the chance to make reasoning—the normally invisible scaffolding of problem-solving—observable and coachable at scale. With formal tools, students can identify where a proof fails, edit the line, and instantly see whether a checker confirms or rejects the fix. Teachers, facing overloaded rosters, can reallocate time from marking to mentoring. Policymakers can define success not as "AI correctness" but as student transfer: the ability to recognize invariants, choose lemmas wisely, and explain why a tactic applies. This reframing turns elite-benchmark breakthroughs into pragmatic classroom levers. It also acknowledges limits: outside math, where correctness admits no oracle, explanation will be probabilistic and contestable. Hence, the need arises for abstention-aware systems, domain-specific verifiers where they exist, and professional development that equips teachers with the language of uncertainty. Progress on autoformalization and prover-in-the-loop pipelines is the technical foundation; human judgment remains the ultimate authority.

Back to the statistic, forward to action

A year ago, the top AI could only achieve a silver standard at the IMO; this summer, two laboratories surpassed the gold standard, while many young competitors still surpassed them. This statistic is illuminating not because it predicts AGI, but because it shows the nature of genuine advancement: narrow fields with dependable verification are yielding to systematic exploration and principled restraint. Educational institutions should react similarly. View math-capable AI as an enhanced calculator with logs, rather than as an oracle; require metrics on refusals and proof traces; enhance teacher capabilities so that recovered time can be transformed into focused feedback; and demand independent verification for any claims of innovation. By aligning procurement, teaching methods, and policy with this understanding, Olympiad gold will benefit students rather than lead us into overstatements. The immediate goal is not general intelligence; it is broad reasoning literacy across a system that is still healing from significant educational setbacks. That is the achievement worth pursuing.

The views expressed in this article are those of the author(s) and do not necessarily reflect the official position of the Swiss Institute of Artificial Intelligence (SIAI) or its affiliates.

References

Ars Technica. (2025, July). OpenAI jumpthe s gun on International Math Olympiad gold medal announcement.

Axios. (2025, July). OpenAI and Google DeepMind race for math gold.

CBS News. (2025, July). Humans triumph over AI at annual math Olympiad, but the machines are catching up.

DeepMind. (2024, July). AI achieves silver-medal standard solving International Mathematical Olympiad problems.

DeepMind. (2025, July). Advanced version of Gemini with Deep Think officially achieves gold-medal standard at the International Mathematical Olympiad.

National Assessment of Educational Progress (NAEP). (2024). Mathematics Assessment Highlights—Grade 4 and 8, 2024.

OECD. (2023). PISA 2022 Results (Volume I): The State of Learning and Equity in Education.

OECD. (2024). Education Policy Outlook 2024.

UNESCO. (2023; updated 2025). Guidance for generative AI in education and research.

Xin, H., et al. (2024). DeepSeek-Prover: Advancing Theorem Proving in LLMs (arXiv:2405.14333).

Yadkori, Y. A., et al. (2024). Mitigating LLM Hallucinations via Conformal Abstention (arXiv:2405.01563).

Zheng, S., Tayebati, S., et al. (2025). Learning Conformal Abstention Policies for Adaptive Risk (arXiv:2502.06884).