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Sharpe, B. T., Mathews, R. & Horne, G. (2026). Artificial Intelligence in Applied Cognitive Psychology: A Commentary,
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Review Article
Received: March 14, 2026.
Revised: May 04, 2026.
Accepted: May 06, 2026.
UDC:
159.9:004.8
10.23947/2334-8496-2026-14-1-115-124
© 2026 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*
Corresponding author: g.horne@chi.ac.uk
Abstract: AI integration in applied cognitive psychology demands critical evaluation beyond efficiency metrics.
Despite widespread institutional adoption, emerging research reveals concerning patterns including high hallucination rates,
deteriorating retention with prolonged exposure, and a consistent tendency to support surface-level task completion at
the expense of deeper cognitive processing. These findings align with established principles regarding desirable difficul
-
ties, metacognitive monitoring, skill acquisition, and vigilance, suggesting that applications prioritising task completion over
cognitive development risk undermining the adaptive expertise essential for complex professional contexts. Methodological
weaknesses in existing research, including brief interventions, inadequate control comparisons, and reliance on satisfaction
measures, further constrain confident conclusions. Nonetheless, several domains including cognitive accessibility, rehabilita-
tion, vigilance, and adaptive tutoring represent areas of genuine promise where AI’s architecture may complement rather
than conflict with established cognitive science. This commentary synthesises emerging evidence, examines methodological
limitations, proposes research priorities for responsible integration, and reflects on where cautious optimism is warranted.
Keywords: Artificial Intelligence, Applied Cognitive Psychology, Expertise Development.
Benjamin T. Sharpe
1,2
, Mathews Rod
3
, George Horne
1*
1
Faculty of Social Science and Humanities, University of Chichester, Chichester, UK,
e-mail: b.sharpe@chi.ac.uk, g.horne@chi.ac.uk
2
Faculty of Health, Southern Cross University, Lismore, Australia
3
Department of Information and Learning Technologies, University of Chichester, Chichester, UK,
e-mail: R.matthews@chi.ac.uk
Artificial Intelligence in Applied Cognitive Psychology: A Commentary
Introduction
The rapid proliferation of generative artificial intelligence systems (Kasneci et al., 2023) presents
applied cognitive psychology with methodological opportunities and conceptual challenges across educa-
tional, occupational, clinical, and research domains. This commentary examines AI integration through ap-
plied cognitive psychology’s evidence-based frameworks, focusing specifically on generative AI systems
(large language models such as ChatGPT) and AI-enhanced educational tools currently being adopted
across institutions. Amidst technological enthusiasm and institutional pressure for AI adoption, applied
cognitive psychology can provide sceptical, evidence-based analysis.
Institutional pressures for AI adoption manifest through administrative mandates for technology
integration, competitive positioning amongst peer institutions, and commercial partnerships with technol-
ogy providers, often proceeding without rigorous evaluation of cognitive outcomes (Williamson, 2021).
These commercial relationships create conflicts between vendors’ financial interests in widespread adop-
tion and cognitive psychology’s scientific obligation to assess genuine educational and professional ben-
efits. Technology companies invest substantial resources in marketing AI tools to educational institutions
and professional organisations, often emphasising efficiency gains whilst downplaying or ignoring cogni-
tive development outcomes (Jobin et al., 2019). When universities and organisations adopt AI systems
through commercial contracts before rigorous empirical evaluation, they prioritise market positioning over
evidence-based decision-making, whilst also placing at risk the statutory privacy rights of students, staff,
and service users whose data enters commercial systems without adequate scrutiny.
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Sharpe, B. T., Mathews, R. & Horne, G. (2026). Artificial Intelligence in Applied Cognitive Psychology: A Commentary,
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Applied cognitive psychology must resist becoming complicit in these premature adoptions driven
by commercial interests rather than demonstrated cognitive benefits. The field’s credibility depends on
maintaining independence from technology industry narratives and insisting on rigorous empirical eval-
uation before endorsing widespread AI integration. As a discipline concerned with understanding and
enhancing human cognitive functioning across domains including memory, attention, learning, decision-
making, vigilance, and metacognition, we must evaluate AI through established cognitive science frame-
works whilst reconsidering foundational assumptions about human-AI interaction.
Current practice reveals troubling disconnection between evidence and implementation: institutions
adopt AI based on efficiency gains whilst ignoring cognitive development outcomes; organisations implement
decision-support without assessing impacts on professional judgement or situational awareness; technology
companies promote tools without addressing skill atrophy or dependency. This commentary synthesises
emerging empirical evidence, examining four questions: (1) What do systematic investigations reveal about
AI’s cognitive impacts? (2) How do findings relate to established principles? (3) What methodological limita-
tions constrain conclusions? (4) What research priorities should guide responsible integration?
Empirical Evidence on AI in Cognitive Psychology Contexts
Recent meta-analytic evidence provides initial empirical grounding for understanding AI’s impact
on learning outcomes. Synthesis of 51 studies revealed that AI systems demonstrate large positive effects
on learning performance (g = 0.867) but substantially smaller effects on ‘deeper cognitive processing’,
more specifically, a moderately positive effect on learning perception (g = 0.456) and on higher-order
thinking (g = 0.457; Wang and Fan, 2025). This differential impact pattern aligns with cognitive psychol-
ogy’s longstanding distinction between surface and deep learning (Marton and Säljö, 1976; Yang et al.,
2024), suggesting that AI may facilitate utilisation of factual knowledge whilst neglecting deeper under-
standing, learning, and critical reflection which would encourage long-term professional development
(Li et al., 2024; Marshall et al., 2022). AI usage could be a cognitive crutch (Barcaui, 2025), rather than
scaffolding deeper learning. Of particular concern is the risk that users operating at a surface level of en-
gagement may uncritically accept AI-generated content, including hallucinated claims, as factual, with this
risk amplifying in proportion to the consequentiality of the decision or claim involved. It is worth noting that
the meta-analysis itself has attracted methodological scrutiny, with critics noting that the included studies
were not assessed for peer-review status, randomisation, or statistical power, a limitation that warrants
cautious interpretation of the effect size estimates.
This pattern holds concerning implications beyond educational contexts: if AI similarly facilitates
surface-level task completion in professional settings whilst undermining development of deep domain
expertise, organisations may cultivate workforces capable of executing routine procedures using AI assis-
tance yet lacking the adaptive expertise required when AI systems fail, prove inapplicable, or when novel
situations demand flexible problem-solving. This concern maps onto Argyris and Schön’s (1978) distinc-
tion between single-loop and double-loop learning: AI may reinforce single-loop responses, in which er-
rors are corrected within existing assumptions, whilst impeding the deeper, paradigm-questioning reflec-
tion characteristic of double-loop learning that is essential for adaptive professional expertise.
This pattern gains theoretical significance when considered alongside cognitive load theory
(Sweller, 1988; Sweller et al., 1998) and research on desirable difficulties in learning (Bjork, 1994; Bjork
and Bjork, 2011, 2020). If AI systems complete cognitive operations that learners or professionals would
otherwise perform, generating explanations, constructing connections, monitoring comprehension, then
reduced cognitive effort may paradoxically undermine the very processing that supports durable learn-
ing and transfer. Yang et al. (2024) distinguish surface from deep learning approaches with generative
AI, suggesting that effectiveness depends critically on pedagogical or operational integration rather than
mere AI tool availability.
Longitudinal evidence further complicates simple assessments of AI’s value for cognitive perfor-
mance and development. Akgun and Toker (2024) documented that whilst pretesting before AI use en-
hanced retention, consistent with testing effect research (Roediger and Karpicke, 2006), prolonged AI
exposure led to memory decline in their sample of 73 undergraduates. This finding suggests potential
time-dependent effects wherein initial benefits may give way to dependency or reduced cognitive en-
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Sharpe, B. T., Mathews, R. & Horne, G. (2026). Artificial Intelligence in Applied Cognitive Psychology: A Commentary,
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gagement, paralleling concerns about calculators and GPS navigation systems affecting mathematical
and spatial cognition respectively. Recent research on cognitive offloading to external tools demonstrates
that whilst offloading can improve immediate task performance, it frequently results in reduced memory
for offloaded information (Grinschgl et al., 2021). Experimental evidence shows that increasing cognitive
offloading improves performance but diminishes subsequent memory, with participants showing lower re-
call for offloaded material compared to internally processed information. Research on cognitive offloading
reveals that whilst offloading can temporarily free cognitive resources, it frequently results in skill atrophy
when individuals become dependent on tools for operations they previously performed internally (Risko
and Gilbert, 2016; Gilbert et al., 2023). Grinschgl et al. (2021) demonstrated that reducing offloading costs
led to increased offloading behaviour alongside improved immediate performance but significantly dimin-
ished memory in subsequent tests. Ward et al. (2017) found that mere smartphone presence reduces
available cognitive capacity even when devices remain unused. These findings suggest AI availability may
create similar cognitive costs through reduced encoding effort and increased dependency. In occupational
contexts, such patterns are particularly concerning emergency responders, medical professionals, or sys-
tem operators who rely on AI decision-support may experience degraded performance when AI systems
malfunction or when situations demand rapid independent judgement without technological assistance.
Research on AI’s impact on critical thinking reveals nuanced patterns requiring careful interpretation.
Essel et al.’s (2024) investigation with undergraduates identified improvements in question formulation and
information analysis alongside concerning patterns of passive acceptance of AI-generated responses.
Similarly, Ododo et al. (2024) documented threats to critical thinking when students uncritically accepted AI
information without analytical engagement or scrutiny. These findings resonate with decades of cognitive
psychology research emphasising that critical thinking develops through active engagement with challeng-
ing material rather than passive consumption of information (Chi, 2009). In professional contexts requiring
complex judgement, management decision-making, clinical assessment, safety-critical evaluation, uncriti-
cal acceptance of AI recommendations without independent verification represents significant risk.
From a metacognitive perspective, AI systems’ inability to model metacognitive monitoring repre-
sents a fundamental limitation (Exintaris et al., 2023). Metacognition, encompassing monitoring of one’s
understanding, recognition of knowledge boundaries, and strategic regulation of cognitive processes,
has been established as central to effective learning and expert performance (Flavell, 1979; Schraw and
Dennison, 1994). Research on metacognitive calibration demonstrates that professionals who accurately
assess their understanding show superior outcomes compared to those with poor confidence-accuracy
correspondence (Baars et al., 2014). The illusion of knowing, wherein individuals feel confident despite in-
complete understanding, becomes particularly problematic when AI systems provide immediate answers
without requiring users to evaluate their own knowledge state first. When AI systems confidently present
inaccurate information without appropriate uncertainty calibration, they model precisely the metacognitive
dysfunction that cognitive psychology seeks to prevent.
Systematic investigation of AI accuracy reveals substantial concerns for cognitive psychology ap-
plications. A recent systematic review documented hallucination rates ranging from 28-91% across AI mod-
els, with considerable variance depending on task complexity and domain (Chelli et al., 2024). Specifically,
these rates were 39.6% for GPT-3.5, 28.6% for GPT-4, and 91.4% for Bard, indicating substantial variation
in reliability across models and making the upper bound largely attributable to an earlier-generation sys-
tem. Examination of ChatGPT citations found 32.3% were fabricated (MacDonald, 2023), whilst broader
systematic reviews indicate that whilst AI can reduce literature review workload by 60-65%, precision
varies dramatically from 4.6% to 88% depending on task characteristics (Chelli et al., 2024). These ac-
curacy limitations present challenges for applied cognitive psychology, where interventions, organisational
recommendations, and clinical applications must be grounded in accurate representation of research evi-
dence. When AI systems conflate distinct theoretical constructs, misattribute empirical findings, or fabricate
research that appears plausible to non-experts, they undermine the evidence-based foundation essential
to responsible professional practice, and raise fundamental questions about the conditions under which AI-
derived output can be treated as information rather than raw data. Without robust supervisory frameworks
and reflexive evaluation practices, practitioners risk ascribing evidential weight to content that is, at best,
unverified and, at worst, systematically misleading. In safety-critical domains, incorrect AI recommenda-
tions regarding vigilance maintenance, workload management, or decision-support system design could
compromise operational safety, particularly in settings such as lifeguarding where failure to detect a hazard
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can lead to permanent injury or death (Sharpe et al., 2024; Vansteenkiste et al., 2025).
Beyond individual cognitive impacts, AI integration raises substantial equity concerns that applied
cognitive psychology must address. Works on digital divides demonstrates that technology access fol-
lows existing socioeconomic stratification patterns (Van Dijk, 2020). Critical discussions of AI in education
consistently identify digital equity as a major challenge, with AI tools requiring subscription costs, high-
bandwidth internet, or advanced digital literacy potentially exacerbating rather than reducing educational
and professional inequalities (Rottner et al., 2025). Individuals from under-resourced communities may
lack access to AI tools their more privileged peers utilise, potentially widening achievement gaps and limit-
ing social mobility (Mostafa, 2025). Furthermore, AI systems trained predominantly on data from Western,
educated, industrialised, rich, and democratic populations may perform poorly for culturally and linguisti-
cally diverse users, embedding bias into ostensibly objective technological systems (Rottner et al., 2025).
The historical record on which LLMs are predominantly trained reflects not only geographic and economic
stratification but also a substantial gender skew, given that recorded history, published scholarship, and
digitised public discourse have been disproportionately produced by and about men, creating conditions
in which gender bias may be structurally encoded rather than incidental. Applied cognitive psychology’s
commitment to understanding human cognition across diverse populations demands critical examination
of who benefits from AI integration and who faces exclusion or disadvantage. Premature adoption without
addressing equity concerns risks institutionalising technological advantages for already-privileged groups
whilst disadvantaging those most needing educational and professional support.
Methodological Limitations of Current AI Research
Critical examination of existing AI research reveals substantial methodological weaknesses that
constrain confident conclusions about AI’s cognitive impacts. Many studies employ brief interventions,
typically single sessions or fewer than four weeks, insufficient for assessing impacts on skill development
or knowledge consolidation. These brief timeframes may capture initial novelty effects or short-term per-
formance changes whilst missing longer-term patterns including skill atrophy, dependency development,
or metacognitive deterioration. In occupational contexts where expertise develops over months or years
through extensive deliberate practice, such abbreviated studies cannot adequately assess AI’s impact on
professional competence.
Control group selection in AI research frequently proves inadequate for establishing AI’s unique
contribution. Many studies compare AI use against no intervention rather than against evidence-based al-
ternatives, creating situations where any active intervention would likely demonstrate superiority over pas-
sive control conditions. Rigorous evaluation requires comparing AI against validated approaches: worked
examples, practice testing, or spaced repetition in educational contexts; established decision-support
systems or expert consultation in occupational settings. Without such comparisons, apparent AI benefits
may simply reflect general support rather than AI-specific advantages.
Outcome assessment represents another critical limitation. Many AI studies rely on self-reported
satisfaction, perceived usefulness, or intention to continue use rather than objective cognitive performance
measures. Whilst subjective experience matters, it proves insufficient for evaluating cognitive impact. Re-
search consistently demonstrates that learners poorly judge which study strategies produce durable learn-
ing (Koriat and Bjork, 2005; Soderstrom and Bjork, 2015), often preferring approaches that create illusions
of competence over those supporting genuine mastery. Similarly, AI users may report high satisfaction
whilst experiencing metacognitive impairment, dependency development, or skill atrophy undetected by
satisfaction measures. Rigorous evaluation demands validated cognitive assessments including retention
tests, transfer tasks, and metacognitive calibration measures administered at delayed timepoints.
Sample characteristics further limit generalisability. Most AI research employs undergraduate con-
venience samples, creating uncertainty about findings’ applicability to professional contexts requiring ex-
tensive domain expertise. University students and domain experts differ substantially in prior knowledge,
metacognitive sophistication, and task approach strategies, factors that may moderate AI’s cognitive
impact. A medical consultant’s interaction with AI decision-support likely differs fundamentally from an
undergraduate’s use of ChatGPT for essay writing. Extrapolating from student samples to professional
contexts risks overlooking critical expertise-related moderators.
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Publication bias represents a pervasive concern across AI research. The methodological enthusi-
asm surrounding AI, combined with commercial interests and institutional pressures, creates conditions
favouring positive findings. Studies documenting AI limitations, null effects, or negative outcomes may
face publication barriers, potentially biasing the evidence base towards optimistic conclusions. This sys-
tematic bias undermines meta-analytic synthesis and evidence-based decision-making.
Theoretical Foundations and Cognitive Science Principles
Established cognitive science principles provide frameworks for understanding AI’s impacts and
limitations. The concept of desirable difficulties (Bjork, 1994;
Bjork and Bjork, 2011, 2020
) illustrates that
conditions impeding immediate performance often enhance long-term retention and transfer. Strategies in-
cluding generation, spacing, interleaving, and variation create processing demands that, whilst temporarily
reducing performance, strengthen memory and promote flexible knowledge application. If AI systems elimi-
nate these beneficial difficulties, providing immediate answers, removing retrieval demands, minimising
error correction, they may inadvertently undermine the cognitive processing essential for durable learning.
The testing effect (Roediger and Karpicke, 2006; Rowland, 2014) demonstrates that retrieval prac-
tice produces superior retention compared to repeated study, even when retrieval proves difficult and er-
ror-prone. AI systems that provide immediate answers without requiring retrieval bypass this mechanism,
potentially reducing long-term retention. Similarly, research on worked examples and problem-solving
(Sweller and Cooper, 1985; Kalyuga et al., 2003) reveals complex interactions between learner expertise
and instructional support. Whilst worked examples benefit novices, they impede expert learning, a pattern
termed the expertise reversal effect. AI assistance may similarly help novices whilst limiting expert devel-
opment if it eliminates the problem-solving practice essential for building adaptive expertise.
Metacognitive monitoring (Flavell, 1979; Koriat, 2007) enables effective learning through accu-
rate assessment of understanding and strategic regulation of study behaviour. When AI systems provide
answers without prompting metacognitive evaluation, users may develop inflated confidence despite in-
complete understanding. This metacognitive impairment extends beyond education: professionals using
AI decision-support may experience reduced calibration between confidence and accuracy, potentially
leading to overreliance on AI recommendations and diminished independent judgement.
Research on skill acquisition (Ericsson et al., 1993) emphasises that expertise requires extensive
deliberate practice with immediate feedback on challenging tasks within one’s domain. If AI completes
tasks that would otherwise constitute deliberate practice, users may never develop the skills essential for
independent performance. This concern proves particularly salient in professional contexts where AI may
facilitate task completion whilst preventing the practice essential for building adaptive expertise.
Vigilance research (Warm et al., 2008; Hancock, 2013) reveals that sustained attention degrades
over time, particularly for rare events in monotonous contexts. AI systems designed to support vigilance
may inadvertently reduce operators’ attentional engagement, leading to greater performance decrements
when AI fails or proves unavailable. In safety-critical domains including lifeguarding, aviation, and medical
monitoring, such vigilance decrements could compromise safety (Sharpe et al., 2026).
AI integration raises fundamental epistemological concerns for cognitive psychology’s scientific
foundations. Traditional scientific understanding emphasises transparent, reproducible reasoning pro-
cesses that can be evaluated, critiqued, and refined. AI systems, particularly large language models, func-
tion as “black boxes” whose decision-making processes remain opaque even to their developers (Rudin,
2019; Ahmed et al., 2022). This opacity fundamentally conflicts with scientific principles of explanatory
understanding. When AI provides correct answers through inscrutable processes, it shifts emphasis from
explanatory knowledge to predictive performance, a transition that may undermine scientific literacy and
critical evaluation skills central to cognitive psychology training. Recent analyses of explainable AI meth-
ods reveal that whilst techniques exist to increase transparency, many create false impressions of under-
standing through inconsistent or misleading explanations (Durán and Jongsma, 2021). Professionals who
rely on AI-generated insights without understanding underlying mechanisms may struggle to 1.) recognise
when AI fails, 2.) generalise knowledge to novel contexts, or 3.) explain their reasoning to colleagues and
clients. It is also worth noting that failure in AI systems is not binary: an unsubstantiated, ethically compro-
mised, or subtly misleading output represents a form of failure even when superficially plausible, and this
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risk is compounded by the feedback dynamics of large language model training, wherein widely shared or
repeatedly accessed hallucinations may be inadvertently reinforced as training data for subsequent model
iterations, progressively entrenching inaccurate information within the knowledge base on which the field
increasingly relies. This epistemological shift from transparent understanding to opaque prediction repre-
sents a fundamental challenge to applied cognitive psychology’s commitment to evidence-based practice
grounded in comprehensible theoretical frameworks. If practitioners cannot explain how conclusions were
reached or assess the validity of AI-generated recommendations, the field risks losing the explanatory
coherence that distinguishes professional expertise from algorithmic output.
Research Priorities for Responsible AI Integration
Applied cognitive psychology must establish rigorous research priorities to guide responsible AI
integration. Longitudinal studies examining AI’s long-term cognitive impacts across diverse populations
and contexts are essential. Such research should employ validated cognitive assessments administered
at delayed timepoints to capture retention, transfer, and metacognitive effects invisible in immediate per-
formance measures. Studies should examine expertise development trajectories, assessing whether AI
use facilitates or impedes progression from novice to expert performance. Comparative studies must
evaluate AI against evidence-based alternatives rather than passive control conditions. In educational
contexts, comparisons should include worked examples, practice testing, spaced repetition, and other
validated learning strategies. In occupational settings, evaluations should compare AI against established
decision-support systems, expert consultation, and traditional training approaches. Such comparisons will
clarify whether AI provides unique benefits or merely replicates existing effective practices.
Research must examine individual differences moderating AI’s cognitive impact. Expertise, metacog-
nitive sophistication, learning strategies, and domain knowledge likely influence how people interact with AI
and whether interactions prove beneficial or detrimental. Identifying these moderators will enable targeted
recommendations about when, for whom, and under what conditions AI proves appropriate. Ecological va-
lidity demands investigation of AI in authentic professional contexts with domain experts performing realistic
tasks. University student performance on artificial laboratory tasks may poorly predict professional use of AI
for complex judgement, diagnosis, or safety-critical decision-making. Field studies in medical, educational,
legal, and engineering contexts will provide essential evidence about real-world impacts.
Research should examine AI’s effects on collaborative work and team performance. Most current
research focuses on individual users, yet many professional contexts involve teamwork. A conceptually
important distinction concerns whether AI functions as an independent contributor to group cognition, a
kind of synthetic team member whose outputs influence shared mental models, or as a parallel tool avail-
able individually to each member, with substantially different implications for group dynamics, accountabil-
ity, and collective bias depending on the answer, particularly where a single proprietary system shapes the
informational environment of all members simultaneously. AI’s impact on communication, coordination,
shared mental models, and collective performance remains largely unexplored. Mechanism-focused re-
search should identify the cognitive processes through which AI affects learning, expertise development,
and professional performance. Understanding whether AI impacts primarily encoding, retrieval, metacog-
nition, attention, or problem-solving strategies will inform more effective AI design and implementation.
Intervention research should examine pedagogical and operational strategies for maximising AI benefits
whilst minimising cognitive costs. Such research might explore optimal timing of AI introduction, appro-
priate scaffolding strategies, methods for maintaining metacognitive engagement, and approaches for
preventing unhealthy dependency or skill atrophy.
Beyond empirical research, applied cognitive psychology must engage with governance and reg-
ulatory frameworks for AI in professional practice. Currently, minimal professional guidelines exist re-
garding appropriate AI use in psychological assessment, intervention design, clinical decision-making,
or research synthesis (Meskó and Topol, 2023). Analysis of 793 state-level AI bills in the United States
identified only 28 explicitly referencing mental health applications, with substantial gaps in professional
oversight, harm prevention protocols, and data governance (Shumate et al., 2025). Meta-analysis of 200
global AI governance documents reveals widespread calls for accountability, transparency, and fairness,
yet most guidelines remain non-binding recommendations rather than enforceable standards (Jobin et al.,
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2019). This regulatory vacuum creates risks including inappropriate delegation of professional judgement
to AI systems, inadequate informed consent when AI contributes to clinical decisions, and unclear ac-
countability when AI-assisted interventions produce adverse outcomes. Professional psychology organi-
sations must develop evidence-based guidelines specifying when AI proves appropriate, what safeguards
protect clients and research participants, and how practitioners maintain competence and professional
responsibility, legal obligations under applicable data protection and privacy law, and adherence to the
ethical principles enshrined in professional codes of practice when utilising AI tools (Martinez-Martin et
al., 2020). Such guidelines should address mandatory training requirements for AI use in professional
contexts, standards for evaluating AI system validity and reliability, and protocols for monitoring AI impacts
on client outcomes. Applied cognitive psychology must also contribute to broader regulatory discussions,
ensuring that AI deployment in educational, clinical, and occupational settings reflects genuine under-
standing of cognitive impacts rather than uncritical technological enthusiasm. Without proactive engage-
ment in governance development, the field risks having AI regulation imposed by policymakers lacking
psychological expertise, potentially mandating practices inconsistent with cognitive science evidence.
The Authors Optimism
It is worth acknowledging upfront that the applications highlighted in this subsection are inevitably
shaped by the authors’ own research interests and should be read accordingly. With that caveat noted,
genuine promise does exist alongside the documented limitations and identifying where AI’s cognitive
footprint may differ substantially from the educational and professional contexts critiqued above is not
a rhetorical gesture towards balance but a precondition for directing the research agenda towards the
most consequential questions. One underappreciated area of genuine promise concerns AI’s potential to
support its own quality assurance. There is a credible case that AI systems, embedded within appropri-
ately designed supervisory frameworks, could be leveraged to detect the probabilistic signatures of hal-
lucinated content, flag claims that fall outside the evidential range of reliable training data, and generate
calibrated uncertainty statements that prompt rather than suppress critical evaluation. Such an application
would not resolve the foundational epistemological concerns raised elsewhere in this commentary, but it
would represent a meaningful step towards reflexive AI deployment, one in which the technology partici-
pates in the governance of its own outputs rather than leaving that burden entirely to users who may lack
the domain knowledge to identify what they are being given.
Cognitive accessibility and neurodivergent populations represent one of the most compelling and
underexamined areas of opportunity. For individuals with dyslexia, ADHD, autism spectrum conditions, or
acquired cognitive impairments, AI-driven tools including adaptive text reformatting, real-time summarisa-
tion, and executive-function scaffolding offer the potential to reduce extrinsic cognitive load in ways that
may genuinely support rather than bypass cognitive engagement. The desirable difficulties framework may
apply quite differently when the difficulty being reduced is a structural barrier rather than a productive cogni-
tive challenge, and applied cognitive psychology has the theoretical vocabulary to articulate this distinction
precisely, yet this literature has developed largely in isolation from mainstream AI cognition research. Fur-
ther, AI-personalised cognitive training and rehabilitation programmes offer a second area of considerable
promise. Unlike general-purpose LLMs, systems designed for dementia prevention, acquired brain injury
rehabilitation, or age-related cognitive decline are structured explicitly to exercise cognitive mechanisms
rather than offload them, making the dependency and skill atrophy concerns raised throughout this com-
mentary considerably less salient. The capacity of AI to personalise difficulty trajectories, monitor engage-
ment, and deliver intervention at scale addresses a genuine limitation of traditional programme delivery.
A third underexplored application lies in vigilance augmentation within safety-critical domains. The
distinction between AI as attentional substitute and AI as perceptual amplifier maps directly onto existing
cognitive theory around levels of automation and situation awareness, representing a domain where vigi-
lance research could yield practically significant design principles across lifeguarding, air traffic control,
and intensive care monitoring. Finally, adaptive intelligent tutoring systems that embed retrieval practice,
spacing, and metacognitive prompting represent an application where AI architecture can be aligned with
established learning science rather than placed in tension with it. These systems predate the current wave
of generative AI and carry a more robust empirical foundation. The prospect of realising such applications
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responsibly is itself a compelling reason for the field to invest in exactly the methodological standards and
governance frameworks this commentary advocates.
Conclusion
AI integration in applied cognitive psychology requires evidence-based evaluation of cognitive im-
pacts rather than uncritical adoption driven by technological enthusiasm or institutional pressures. Cur-
rent evidence reveals differential effects, with AI supporting surface learning more effectively than deeper
cognitive processing. Substantial methodological limitations constrain confident conclusions about AI’s
long-term impacts on expertise development, metacognition, and professional performance. Established
cognitive science principles regarding desirable difficulties, testing effects, metacognitive monitoring, skill
acquisition, and vigilance provide frameworks for understanding both AI’s potential benefits and risks.
Critical analysis reveals concerning equity implications, epistemological challenges, and governance
gaps that demand urgent attention.
Responsible integration demands systematic investigation addressing current methodological
weaknesses through longitudinal designs, appropriate control comparisons, objective cognitive assess-
ments, diverse samples, and authentic professional contexts. Applied cognitive psychology must resist
institutional pressures for premature adoption, instead providing sceptical, evidence-based analysis of
when, for whom, under what conditions, and through what mechanisms AI enhances or undermines
cognitive functioning. Only through rigorous empirical investigation and proactive governance engage-
ment can the field fulfil its responsibility to promote genuine cognitive development rather than mere task
completion in an increasingly AI-integrated world.
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