ARTIFICIAL INTELLIGENCE
IN EDUCATION
A Neuroeducational Perspective for Transforming Digital Tools into Learning Opportunities
Academic Essay with a Contextualized Case Study
CONALEP Campus 156 · Hidalgo del Parral, Chihuahua
September 2026
Educational Content · Academic Working Document
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Abstract and Purpose
Artificial intelligence is already part of students’ cognitive ecosystem. Its educational relevance does not lie in
replacing teachers or indiscriminately automating tasks, but in expanding opportunities for feedback,
differentiation, creation, search, and practice when there is a clear pedagogical intention. From a
neuroeducational perspective, the decisive question is which mental processes a tool activates and which
ones it may displace.
This essay proposes an applied approach for upper-secondary education and uses CONALEP Campus 156
in Hidalgo del Parral, Chihuahua, as its context. In classrooms equipped with screens and where mobile
phones are part of everyday life, the strategy cannot be reduced to prohibition. It is more productive to design
sequences in which technology has visible moments, purposes, and limits.
The case study is a contextualized proposal and does not claim empirical results that have not been
collected. It is presented as an evaluable intervention model with indicators that could later document
changes in learning, self-regulation, and responsible AI use.
In practical terms, this requires distinguishing visible performance from durable learning. A polished product
may conceal little understanding if it was produced almost entirely by an external tool. Evidence of learning
should therefore include moments in which students retrieve, explain, apply, or defend without assistance
what they claim to have learned.
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Introduction: Educating in the Age of Artificial Intelligence
The arrival of generative AI requires us to reconsider what it means to learn when a machine can produce
explanations, summaries, examples, images, exercises, and drafts in seconds. The educational challenge is
no longer only access to information, but learning to formulate questions, contrast answers, detect errors,
and transform information into personal knowledge.
In upper-secondary education, this transformation is especially relevant because young people already use
digital devices inside and outside the classroom. Schools can ignore this reality, fight it without nuance, or
turn it into an object of literacy. The third option is the most consistent with education for contemporary life.
UNESCO proposes a human-centered approach to AI that protects agency, privacy, inclusion, and
opportunities to develop cognitive capacities. This means using AI when it adds value and withdrawing it
when it replaces a mental effort that students need to perform themselves.
Technology offers speed, but teaching provides purpose, sequence, and judgment. When both are
articulated, AI can expand opportunities; when they are confused, efficiency can become a pedagogical
illusion.
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1. AI as a Tool, Not an End
An innovative school is not the one that uses the most applications, but the one that selects tools according
to learning objectives. AI can act as a tutor, example generator, interlocutor, planning assistant, accessibility
support, or feedback mechanism. None of these roles has value by itself without a clearly defined cognitive
task.
For example, asking a chatbot to write a complete answer before a student thinks may reduce retrieval and
elaboration. In contrast, asking the student to respond first and then compare the response with an
AI-generated explanation preserves the initial effort and adds feedback.
The pedagogical rule proposed here is simple: first identify the mental process to be exercised; then decide
whether AI should appear before, during, or after that process.
From a neuroeducational perspective, students should remain cognitively active. Reading, comparing,
deciding, remembering, explaining, and correcting are more important verbs than simply obtaining.
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2. Neuroeducation: Scope and Limits
Neuroeducation integrates findings from neuroscience, cognitive psychology, and educational science to
better understand conditions that support learning. It does not mean assigning every activity to a brain region
or using neurological vocabulary as decoration.
Its value lies in connecting evidence about attention, memory, emotion, practice, and self-regulation with
instructional decisions. An AI tool should therefore be evaluated by its effects on learner activity: Does it
promote retrieval? Does it organize information? Does it reduce irrelevant load? Does it provide feedback
that supports correction? Or does it simply deliver the final product?
The neuroeducational approach avoids two extremes: uncritical technological enthusiasm and automatic
rejection. The question is not whether AI is good or bad, but under which conditions it supports learning
processes that still depend on human cognitive activity.
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3. Attention in a Screen-Based Environment
Attention is limited and selective. Smartphones compete for it through notifications, messages, videos, and
immediate rewards. In the classroom, the problem is not merely the physical presence of the device, but the
constant fragmentation of mental activity.
When classrooms already have screens and students carry phones, it is useful to establish an architecture of
attention: moments with a shared screen, moments with phones face down or stored away, brief consultation
windows, and periods of individual production.
In this way, the phone stops being permanently available and becomes a resource with defined windows of
use. Digital discipline does not mean the absence of technology; it means the ability to decide when to use it
and when to ignore it.
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4. Memory and Active Retrieval
Learning requires retrieving information, not merely seeing it again. Retrieval practice strengthens retention
and can support transfer more effectively than simple restudy. Recent evidence continues to support the
value of attempting to remember and then receiving feedback.
AI can support this practice by generating graded questions, flashcards, mini-tests, or variations of a
problem. However, it should not reveal the answer immediately. A neuroeducational design requires an
interval of effort: first remember, then verify.
In an English class, for example, a teacher can project ten numbers, remove them, ask students to write them
in words, and then use AI to generate new quantities with difficulty adjusted to observed errors.
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5. Immediate and Explanatory Feedback
Feedback is one of the most promising functions of AI. A system can respond quickly to many students,
identify error patterns, and offer differentiated explanations. Research on AI-driven chatbot feedback
suggests that the design of feedback matters, not only its speed.
The best use is not simply to say “correct” or “incorrect.” It is better to request brief explanations: which rule
was applied, what part is correct, what needs revision, and a hint before revealing the solution.
For teachers, this can preserve human time for interventions that require sensitivity, pedagogical diagnosis,
conversation, and accompaniment.
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6. Cognitive Load and Scaffolding
A task may be difficult because of the content being learned or because of unnecessary presentation
demands. Technology can reduce irrelevant cognitive load through clear examples, sequences, visual
supports, or controlled translations. It can also increase load when it introduces too many options, windows,
stimuli, or instructions.
In a classroom with a shared screen, AI can help the teacher present an explanation step by step and
gradually remove support. Students do not need twenty functions at once; they need enough support to move
forward.
The neuroeducational criterion is that scaffolding should be temporary. If AI always solves the task, it stops
being scaffolding and becomes substitution.
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7. Emotion, Curiosity, and Relevance
Emotion influences which information receives attentional priority and whether a learner persists. This does
not mean that every lesson must be entertainment. It means that learning benefits from meaning, curiosity,
attainable challenge, and a perception of progress.
AI can quickly contextualize exercises: numbers connected to the real size of Africa, dialogues related to
adolescent situations, community-based problems, or visual examples close to students’ environments.
The risk appears when motivation is confused with constant stimulation. A neuroeducational lesson combines
novelty with periods of sustained concentration.
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8. Metacognition: Learning to Review One’s Own Thinking
One of the most important competencies in the age of AI is knowing when one truly understands and when
one merely recognizes a convincing answer. Generative models produce fluent text that can create a false
sense of comprehension.
Students therefore need metacognitive routines: explain in their own words, identify what they do not
understand, estimate confidence, check sources, and justify why they accept or reject an answer.
AI can become a metacognitive mirror when it is asked to compare reasoning and ask questions instead of
immediately providing a conclusion.
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9. Executive Functions and Self-Regulation
Planning, inhibiting impulses, maintaining a goal, and changing strategy are essential capacities for learning.
Impulsive phone use competes with them, but regulated use can turn the device into a setting for training
self-control.
A sequence might specify: two minutes to consult, five minutes to work without a screen, one minute to verify,
and three minutes to correct. External structure can help build internal habits.
The goal is not permanent dependence on rigid instructions, but gradual development of students’ ability to
manage their attention and tools.
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10. The Risk of Cognitive Offloading
Cognitive offloading occurs when part of mental work is delegated to an external resource. It is not always
negative: using an agenda or calculator can free resources for higher-level tasks. The problem appears when
the learner externalizes the very skill that is supposed to be learned.
If a student needs to practice writing and asks AI for the entire paragraph, the tool replaces the practice. If the
student writes first, requests feedback, and then revises while justifying changes, AI can strengthen the
process.
The principle is to distinguish between offloading accessory work and offloading the central cognitive
objective.
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11. Generative AI and Critical Thinking
The verbal fluency of an AI system does not guarantee truth. AI literacy must therefore include verification.
Students need to understand that a plausible response can contain invented facts, generalizations, or
nonexistent references.
A powerful activity is to ask AI for an explanation and turn students into auditors: locate verifiable claims,
classify them, and contrast them with reliable sources.
Critical thinking then stops being an abstract concept and becomes a daily reading practice.
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12. Knowing How to Ask: Prompt Literacy
Using AI tools effectively does not mean memorizing magical prompting formulas. It means communicating
an objective, context, constraints, audience, and format, and then evaluating the result.
An educational prompt can include the student’s level, the content, the type of support allowed, and an
instruction not to reveal the answer. Example: “Act as an A1 English tutor. Ask me one question about
numbers. Wait for my answer, give me a hint if I am wrong, and do not answer for me.”
This competence combines language, planning, and evaluation and can therefore be considered a
contemporary form of digital literacy.
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13. Verification and Triangulation
Every AI-generated product should be treated as a draft until it has been verified. Verification may involve a
textbook, institutional source, scientific article, independent calculation, or comparison across evidence.
In the classroom, the phrase “AI said it” should be the beginning, not the end, of an explanation. Students
should be able to answer: How did you verify it?
This practice protects against errors and strengthens research habits.
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14. Ethics, Privacy, and Data
Educational use of AI involves decisions about personal data, age, consent, and exposure of schoolwork.
Students should not enter sensitive data, identifiable information about classmates, or private documents
without authorization.
They also need to understand that different platforms have different policies. Digital competence includes
knowing what not to share.
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15. Authorship, Plagiarism, and Transparency
AI complicates traditional categories of plagiarism because it can produce new text that the student did not
cognitively compose. A useful educational response is not only detection, but redesigning tasks.
Teachers can request drafts, justified decisions, successive versions, oral defense, and a record of AI use.
This allows evaluation of process as well as product.
A simple policy can ask students to declare which tool they used, for what purpose, and which parts they
modified themselves.
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16. The Irreplaceable Role of the Teacher
AI can generate information, but it does not independently know the pedagogical history of a group, its social
dynamics, silences, frustration, or the right moment to insist or stop. Teachers interpret context.
The teacher’s role shifts partly from exclusive transmitter to designer of experiences, mediator, curator of
information, and coach of thinking.
This requires professional development, not professional replacement.
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17. AI for Teacher Planning
One of the most immediate applications of AI is support for planning: generating examples, adapting
instructions, proposing difficulty levels, creating question banks, and anticipating errors.
Time savings are valuable only if the teacher reviews and contextualizes the output. An automatically
generated plan may be technically correct but pedagogically inappropriate for a specific group.
AI should generate options; the final decision remains professional.
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18. Differentiation and Diverse Learning Paces
A single classroom includes students with different prior knowledge, learning rates, and levels of confidence.
AI can generate variations of an activity without changing its objective.
One student may receive ten two-digit numbers while another works with hundreds and thousands. Both
practice the same linguistic structure with an adjusted cognitive load.
Differentiation should not permanently label students; it should offer temporary routes for access and
challenge.
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19. AI and English Language Learning
In language learning, AI can simulate conversations, provide examples, reformulate instructions, and
generate extensive practice. Its usefulness increases when the linguistic level and objective are clearly
constrained.
For A1, for example, prompts can request high-frequency vocabulary, short sentences, and specific
feedback. Students can practice numbers, greetings, personal information, and basic structures.
Real human interaction, authentic pronunciation, and spontaneous production should still be preserved.
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20. AI and Visual Production
In design and communication, visual AI opens possibilities for analyzing composition, style, color, hierarchy,
and narrative. Learning should not be limited to generating images.
A richer activity asks students to compare a human proposal with an AI-generated one, justify decisions, and
edit the result.
Assessment can focus on intention, process, and visual argumentation.
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21. The Mobile Phone: A Real Problem and a Possible Resource
Mobile phones can distract, facilitate copying, and fragment attention. Denying this would be naive. But
precisely because they are present, they can become an object of education.
The idea of making the best of a difficult situation has pedagogical value here: not celebrating distraction, but
transforming an imperfect condition into an opportunity for self-regulation.
Phones can be used for micro-consultations, evidence photos, timers, forms, QR-code reading, or AI
interaction, always within defined time limits.
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22. Classroom Screens as a Shared Resource
Classroom screens allow technology to be shared rather than necessarily individual. A single projected
interaction can become a collective analysis.
The teacher can display a prompt, ask students to predict the response before running it, and analyze the
output with the whole group. This reduces isolation and makes thinking visible.
It also allows AI literacy without requiring every student to have an account or mobile data.
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23. From Prohibition to Protocol
An effective rule specifies observable behavior. “Do not get distracted by your phone” is ambiguous; “for the
next seven minutes, keep your phone face down; when the green icon appears, you will have two minutes to
consult” is operational.
Protocols reduce constant negotiation and help create shared expectations.
Educational phone use should be accompanied by clear consequences when the agreement is broken.
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24. Designing a Neuroeducational Lesson with AI
A lesson can be organized into cycles: activation of prior knowledge, individual attempt, contrast, feedback,
varied practice, and final retrieval.
AI enters after the first attempt, not necessarily before. This protects cognitive effort and turns the tool into
feedback.
The closing task should ask students to produce something without assistance to verify what remains
available in memory.
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25. Microlearning and Spaced Practice
Digital tools facilitate small doses of practice distributed over time. Five questions today and five more in two
days can be more useful than one isolated massed session.
AI can vary examples to prevent superficial memorization and adjust difficulty.
The teacher should maintain a clear map of objectives so that variety does not become dispersion.
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26. Error-Based Learning
Errors provide information about the learner’s mental model. Well-used AI can generate distractors based on
common mistakes and explain why an option fails.
It is important to create a climate in which making mistakes during practice is not equivalent to failing.
Feedback should direct attention to the procedure and allow a second attempt.
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28. Human–AI Collaboration
The most productive model is not human versus machine, but human with machine under human
supervision. The student contributes purpose, experience, judgment, and responsibility; AI contributes speed,
variation, and the capacity to generate alternatives.
Tasks can assign roles: one student asks, another verifies, another improves, and another explains.
In this way, AI becomes part of social collaboration instead of replacing it.
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30. Equity and the Digital Divide
AI can expand opportunities, but it can also expand inequality when some students have better devices,
connectivity, or subscriptions. Essential activities should not depend exclusively on private resources.
Classroom screens can function as shared infrastructure, and teamwork can reduce barriers.
Equity requires equivalent alternatives when a tool is unavailable.
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31. Inclusion and Accessibility
AI can support linguistic simplification, example generation, assisted reading, and format adaptation. These
possibilities can be valuable for diverse needs.
However, automatic adaptations must be reviewed so that they do not lower expectations or introduce errors.
Inclusion means removing barriers while maintaining intellectually meaningful objectives.
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32. Authentic Assessment
When AI can produce a generic essay, assessment needs to move closer to processes and contexts that
require personal decisions and visible evidence of learning.
Written products can be combined with oral defense, learning logs, in-class activities, and application to local
cases.
The goal is not to create tasks that are impossible for AI, but tasks in which student learning is visible.
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33. Rubrics for AI-Supported Work
A rubric can assess the quality of the question, information verification, explanation of changes, originality of
decisions, and reflection on limitations.
This shifts the emphasis from “Did the student use AI?” to “How did the student use it, and what did the
student learn?”
Rules should be communicated before the activity. In the proposed case, evidence must be collected before
improvements are claimed; the observations function as indicators for a future intervention, not as
demonstrated results.
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35. Case Study: Context of CONALEP Campus 156
The case is situated in upper-secondary education in Hidalgo del Parral, Chihuahua. It begins with two
classroom conditions provided for this essay: screens are available in classrooms and students use mobile
phones during the school day.
These conditions create tension: devices can compete with attention, but they also provide immediate
infrastructure for digital activities. The proposed intervention seeks to use this availability without normalizing
unrestricted use.
This case study is prospective and instructional. It does not attribute unmeasured data, percentages, or
results to the campus.
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36. Initial Diagnosis of the Case
Before intervention, it would be useful to observe for two weeks: frequency of non-academic phone use,
moments of greatest distraction, types of tasks, participation, and performance in brief retrieval activities.
An anonymous survey can also explore AI uses: translation, summarization, homework solving, studying,
image generation, or conversation.
The diagnosis should avoid moralizing; its purpose is to improve instructional design.
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37. Intervention Question
The proposed central question is: Can a protocol for intentional mobile-phone and AI use improve
participation and learning quality without increasing technological dependence?
Observable educational variables could include percentage of tasks completed, quality of unaided
responses, ability to detect AI errors, and compliance with phone-use windows.
Neuroscientific instrumentation is not required for an intervention inspired by neuroeducation.
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38. Case Objectives
The general objective is to integrate AI in a pedagogically controlled manner to strengthen retrieval,
feedback, metacognition, and digital literacy.
Specific objectives include teaching basic prompting, establishing attention protocols, practicing response
verification, and comparing performance with and without assistance.
A cross-cutting objective is for students to recognize when AI helps them and when it takes away an
opportunity to think.
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39. Instructional Sequence 1: Think First, Ask Second
The first rule of the intervention would be “Brain first, AI second.” When faced with a question, students write
an initial response without a phone. Then an AI consultation window opens, and finally students correct their
work in another color while explaining the change.
The assessable product is not the AI answer, but the comparison between the initial and final versions.
This sequence turns the tool into feedback while preserving retrieval.
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40. Instructional Sequence 2: Error Detective
The teacher projects an AI-generated response that contains a mixture of correct information and deliberately
introduced or previously verified errors.
Teams identify doubtful claims, search for evidence, and justify corrections.
The exercise develops productive skepticism and reduces the tendency to treat fluent language as authority.
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41. Instructional Sequence 3: Socratic Tutor
Students create a prompt instructing AI to ask questions, offer hints, and wait for answers instead of solving
the task.
In A1 English, this can be used to practice numbers, greetings, or personal information; in other subjects, it
can support concepts and procedures.
The student keeps the primary cognitive turn.
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42. Instructional Sequence 4: Shared Screen
To reduce dispersion, some activities use a single AI system projected on the classroom screen. Before
sending the prompt, the group predicts the response and proposes improvements.
Afterward, the result is evaluated collectively.
This modality uses classroom infrastructure while publicly modeling critical thinking.
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43. Mobile-Phone Protocol
A three-state visual code is proposed: stored, available for a specific task, and free after completion. The
teacher announces the state and the time.
Use windows may last from one to five minutes depending on the task. Afterward, the device is stored again
or placed face down.
The protocol should be applied consistently and periodically reviewed with the group.
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44. Case Assessment Instruments
Possible instruments include brief unaided pre- and post-tests, verification rubrics, self-regulation checklists,
comparison products, and short interviews.
Qualitative examples should also be recorded: which errors students detected, which prompts they improved,
and which students could explain without support.
Data should be analyzed in aggregate and with respect for privacy.
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45. Expected Indicators and Success Criteria
A favorable result would not mean “using more AI.” It would mean observing higher quality in students’ own
responses, stronger verification skills, fewer irrelevant consultations during work windows, and more strategic
tool use.
It would also be valuable for students to verbalize rules such as “I try first,” “I do not share personal data,” and
“I verify before submitting.”
If unaided performance decreases, the intervention should be reconsidered even if AI-assisted products look
better.
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46. Case Risks and Mitigation
The main risks are distraction, dependence, false answers, unequal access, data exposure, and use of AI to
evade tasks.
Mitigation combines instructional design: brief use windows, initial work without AI, verification, shared-screen
options, and process-based assessment.
No policy eliminates every risk; the goal is to reduce them and teach students how to manage them.
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47. Discussion: From External Control to Autonomy
At first, phone use may require signals and times established by the teacher. Over time, the goal is for
students to internalize criteria and make decisions independently.
This transition reflects an important neuroeducational idea: external regulation can function as scaffolding for
self-regulation.
A school that only confiscates opportunities for decision-making may temporarily control behavior without
necessarily teaching students to manage technology outside school.
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48. Institutional Recommendations
The campus could develop brief, shared guidelines for AI: permitted uses, disclosure of assistance, privacy,
assessment, and consequences. Consistency among teachers reduces confusion.
Professional-learning sessions could ask teachers to design a real activity and identify which cognitive
process they want to protect.
Policies should be reviewed periodically because AI tools change rapidly.
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49. Conclusions
Artificial intelligence can enrich education when integrated as a tool for feedback, differentiation, practice, and
creation, but it can impoverish education when it replaces the effort that produces learning.
A neuroeducational approach offers a practical criterion: protect attention, retrieval, elaboration,
metacognition, and self-regulation. The central question is not how much AI to use, but how to design its
intervention.
In the proposed context of CONALEP Campus 156, screens and mobile phones can stop being only a source
of tension and become regulated resources. Making the best of a difficult situation matters, but good
pedagogical design matters even more.
Students should remain cognitively active: reading, comparing, deciding, remembering, explaining, and
correcting. AI is educationally appropriate when it supports these actions without taking them over.
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50. Bibliography and Supporting Sources
Ates, H. (2026). Human-centered GenAI feedback design in higher education: A multisite experiment on direct, reflective, and
hybrid approaches to scientific argumentation. International Journal of Educational Technology in Higher Education, 23, 38.
Endres, T., Kranzdorf, L., Schneider, V., & Renkl, A. (2021). Does individual performance feedback increase the use of retrieval
practice? Educational Psychology Review, 33, 1835–1857.
Morales, C. (2026). Retrieval Interruption Framework: AI-assisted cognition and retrieval-dependent learning in higher education.
Education Sciences, 16(8), 1179.
UNESCO. (2021). AI and education: Guidance for policy-makers. United Nations Educational, Scientific and Cultural
Organization.
UNESCO. (2023). Guidance for generative AI in education and research. United Nations Educational, Scientific and Cultural
Organization. Spanish edition published in 2024.
Yin, J., Xu, H., Pan, Y., et al. (2025). Effects of different AI-driven chatbot feedback on learning outcomes and brain activity. npj
Science of Learning, 10, 17.
Learning and Instruction. (2025). Effects of retrieval practice on retention and application of complex educational concepts, 100,
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Learning and Instruction. (2025). Retrieval practice in stepwise worked examples improves learning, 102196.
Asher, M. W., & Carvalho, P. F. (2026). Conditions for effective learning without upfront instruction: How practice with feedback
supports memory, generalization, motivation, and metacognition. Educational Psychology Review, 38, 12.
Methodological note on the case study. The description of classroom screens and student mobile-phone
use corresponds to the context provided for this document. No measurements, samples, percentages, or
outcomes attributable to CONALEP Campus 156 were invented. The case is presented as an intervention
design that may be implemented and evaluated.
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