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daniyasiddiquiEditor’s Choice
Asked: 15/10/2025In: ,

students can “cheat” with AI,

academic integrityai and cheatingai in educationassessment designedtech ethicsfuture-of-education
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    If Students Are Able to "Cheat" Using AI, How Should Exams and Assignments Adapt? Artificial Intelligence (AI) has disrupted schools in manners no one had envisioned a decade ago. From ChatGPT, QuillBot, Grammarly, and math solution tools powered by AI, one can write essays, summarize chapter contenRead more

    If Students Are Able to “Cheat” Using AI, How Should Exams and Assignments Adapt?

    Artificial Intelligence (AI) has disrupted schools in manners no one had envisioned a decade ago. From ChatGPT, QuillBot, Grammarly, and math solution tools powered by AI, one can write essays, summarize chapter content, solve equations, and even simulate critical thinking — all in mere seconds. No wonder educators everywhere are on edge: if one can “cheat” using AI, does testing even exist anymore?

    But the more profound question is not how to prevent students from using AI — it’s how to rethink learning and evaluation in a world where information is abundant, access is instantaneous, and automation is feasible. Rather than looking for AI-proof tests, educators can create AI-resistant, human-scale evaluations that demand reflection, imagination, and integrity.

    Let’s consider what assignments and tests need to be such that education still matters even with AI at your fingertips.

     1. Reinventing What’s “Cheating”

    Historically, cheating meant glancing over someone else’s work or getting unofficial help. But in 2025, AI technology has clouded the issue. When a student uses AI to get ideas, proofread for grammatical mistakes, or reword a piece of writing — is it cheating, or just taking advantage of smart technology?

    The answer lies in intention and awareness:

    • If AI is used to replace thinking, that’s cheating.
    • If AI is used to enhance thinking, that’s learning.

     Example: A student who gets AI to produce his essay isn’t learning. But a student employing AI to outline arguments, structure, then composing his own is showing progress.

    Teachers first need to begin by explaining — and not punishing — what looks like good use of AI.

    2. Beyond Memory Tests

    Rote memorization and fact-recall tests are old hat with AI. Anyone can have instant access to definitions, dates, or equations through AI. Tests must therefore change to test what machines cannot instantly fake: understanding, thinking, and imagination.

    • Healthy changes are:Open-book, open-AI tests: Permit the use of AI but pose questions requiring analysis, criticism, or application.
    • Higher-order thinking activities: Rather than “Describe photosynthesis,” consider “How could climate change influence the effectiveness of tropical ecosystems’ photosynthesis?”
    • Context questions: Design anchor questions about current or regional news AI will not have been trained on.

    The aim isn’t to trap students — it’s to let actual understanding come through.

     3. Building Tests That Respect Process Over Product

    If we can automate the final product to perfection, then we should begin grading on the path that we take to get there.

    Some robust transformations:

    • Reveal your work: Have students submit outlines, drafts, and thinking notes with their completed project.
    • Process portfolios: Have students document each step in their learning process — where and when they applied AI tools.
    • Version tracking: Employ tools (e.g., version history in Google Docs) to observe how a student evolves over time.

    By asking students to reflect on why they are using AI and what they are learning through it, cheating is self-reflection.

    4. Using Real-World, Authentic Tests

    Real life is not typically taken with closed-book tests. Real life does include us solving problems to ourselves, working with other people, and making choices — precisely the places where human beings and computers need to communicate.

    So tests need to reflect real-world issues:

    • Case studies and simulations: Students use knowledge to solve real-world-style problems (e.g., “Create an AI policy for your school”).
    • Group assignments: Organize the project so that everyone contributes something unique, so work accomplished by AI is more difficult to imitate.
    • Performance-based assignments: Presentations, prototypes, and debates show genuine understanding that can’t be done by AI.

     Example: Rather than “Analyze Shakespeare’s Hamlet,” ask a student of literature to pose the question, “How would an AI understand Hamlet’s indecisiveness — and what would it misunderstand?”

    That’s not a test of literature — that is a test of human perception.

     5. Designing AI-Integrated Assignments

    Rather than prohibit AI, let’s put it into the assignment. Not only does that recognize reality but also educates digital ethics and critical thinking.

    Examples are:

    • “Summarize this topic with AI, then check its facts and correct its errors.”
    • “Write two essays using AI and decide which is better in terms of understanding — and why.”
    • “Let AI provide ideas for your project, but make it very transparent what is AI-generated and what is yours.”

    Projects enable students to learn AI literacy — how to review, revise, and refine machine content.

    6. Building Trust Through Transparency

    Distrust of AI cheating comes from loss of trust between students and teachers. The trust must be rebuilt through openness.

    • AI disclosure statements: Have students compose an essay on whether and in what way they employed AI on assignments.
    • Ethics discussions: Utilize class time to discuss integrity, responsibility, and fairness.
    • Teacher modeling: Educators can just use AI themselves to model good, open use — demonstrating to students that it’s a tool, not an aid to cheating.

    If students observe honesty being practiced, they will be likely to imitate it.

    7. Rethinking Tests for the Networked World

    Old-fashioned time tests — silent rooms, no computers, no conversation — are no longer the way human brains function anymore. Future testing is adaptive, interactive, and human-facilitated testing.

    Potential models:

    • Verbal or viva-style examinations: Assess genuine understanding by dialogue, not memorization.
    • Capstone projects: Extended, interdisciplinary projects that assess depth, imagination, and persistent effort.
    • AI-driven adaptive quizzes: Software that adjusts difficulty to performance, ensuring genuine understanding.

    These models make cheating virtually impossible — not because they’re enforced rigidly, but because they demand real-time thinking.

     8. Maintaining the Human Heart of Education

    • Regardless of where AI can go, the purpose of education stays human: to form character, judgment, empathy, and imagination.
    • AI may perhaps emulate style but never originality. AI may perhaps replicate facts but never wisdom.

    So the teacher’s job now needs to transition from tester to guide and architect — assisting students in applying AI properly and developing the distinctively human abilities machines can’t: curiosity, courage, and compassion.

    As a teacher joked:

    • “If a student can use AI to cheat, perhaps the problem is not the student — perhaps the problem is the assignment.”
    • That realization encourages education to take further — to design activities that are worthy of achieving, not merely of getting done.

     Last Thought

    • AI is not the end of testing; it’s a call to redesign it.
    • Rather than anxiety that AI will render learning obsolete, we can leverage it to make learning more real than ever before.
    • In the era of AI, the finest assignments and tests no longer have to wonder:

    “What do you know?”

    but rather:

    • “What can you make, think, and do — AI can’t?”
    • That’s the type of assessment that breeds not only better learners, but wise human beings.
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mohdanasMost Helpful
Asked: 09/12/2025In:

a boon or a bane for education

ai in educationbenefits and risksedtechethics in aiteaching and learningtechnology impact
  1. mohdanas
    mohdanas Most Helpful

    1. Why Many See AI as a Powerful Boon for Education 1. Personalized Learning on a Scale Never Before Possible Education has followed a mass-production model for centuries: one teacher, one curriculum, one pace for dozens of students, regardless of individual differences. AI changes this fundamentallRead more

    1. Why Many See AI as a Powerful Boon for Education

    1. Personalized Learning on a Scale Never Before Possible

    Education has followed a mass-production model for centuries: one teacher, one curriculum, one pace for dozens of students, regardless of individual differences. AI changes this fundamentally.

    With AI,

    • A struggling student can receive slower, adaptive explanations.
    • A high-performing student can go faster without being held back.
    • The visual learners, auditory learners, and hands-on learners can be supported differently.

    This is revolutionary in the sense that it turns education from being a rigid system to a responsive one. Students will no longer be forced to conform to a single learning speed or style.

    2. Instant Feedback Accelerates Growth

    In traditional settings, students can wait days or even weeks for feedback on assignments. AI offers:

    • Real-time corrections
    • Tracking progress continuously
    • Immediate explanation of errors

    And when feedback is instantaneous, learning improves dramatically. Mistakes become learning moments, not ongoing confusion. This alone makes AI a major educational upgrade.

    3. Access for the Previously Excluded

    AI is opening doors for learners who were previously disadvantaged:

    • Students from rural or remote areas
    • Working professionals who cannot attend full-time classes.
    • Students with disabilities requiring assistive technologies
    • Learners across linguistic boundaries through real-time translation.

    With AI, millions around the world are experiencing quality education for the very first time. In this regard, AI is less an indulgence and more of an equalizing force.

    4. Teachers Become Mentors, Not Just Graders

    • AI can automate
    • Grading
    • Attendance
    • Test creation
    • Repetitive explanations

    This frees up the teachers to:

    • Critical discussion
    • Emotional support
    • Deep conceptual teaching
    • Creativity and mentorship

    Well used, AI does not replace teachers; it restores the most human part of teaching.

    2. Why Others Fear AI as a Serious Bane

    Now, the shadow side because the danger is real.

    1. The Erosion of Deep Thinking

    Not all learning is meant to be easy. Struggle is an element of growth-it is how the brain grows. When students constantly employ AI for

    • Writing essays
    • Problem solving
    • Generating ideas instantly

    They risk skipping the very mental effort that builds:

    • Critical thinking
    • Logical reasoning
    • Intellectual endurance

    Over time, this can produce students who know how to get answers but not how to think.

    2. Creativity at the Risk of Becoming Artificial

    Creativity grows from:

    • Imagination
    • Curiosity
    • Boredom
    • Experimentation
    • Failure

    If AI constantly supplies:

    • Stories
    • Art
    • Designs
    • Research ideas

    The students risk becoming editors of machine output rather than true creators. The danger is subtle: human originality gives way, bit by bit, to algorithmic convenience.

    3. Academic Integrity in Crisis

    This is one of the most immediate and visible threats:

    • AI-written essays
    • Auto-generated code assignments
    • Machine-produced research summaries

    It has become increasingly challenging to differentiate between:

    • Student Effort
    • Machine output
    • This creates:
    • Unfair advantages
    • Credential dilution

    Loss of trust between the students and institutions.

    With the collapse of trust, the whole assessment system turns fragile.

    4. Widening the Digital Divide

    AI can democratize learning-but only for the people who can access it.

    • Without
    • Reliable Internet
    • Devices
    • Digital Literacy

    AI becomes another force that amplifies inequality instead of reducing it. Most of the benefits would devolve onto those students who are already at an advantage, while others fall behind.

    3. The Core Truth: AI Is a Tool, Not a Teacher

    AI does not have:

    • Wisdom
    • Values
    • Ethics
    • Purpose
    • Responsibility

    It only reflects:

    • The data it was trained on
    • The goals the humans give it
    • The way institutions deploy it

    Used as:

    • A shortcut → it weakens learning
    • A thinking partner → strengthens learning.
    • A substitute for effort → it hollows education
    • A scaffold for growth → it amplifies intelligence

    AI is a cognitive amplifier; it amplifies what already exists in a learner and in a system.

    4. When AI Truly Becomes a Boon

    AI enhances education when:

    • Students must attempt problems before viewing AI solutions
    • Teachers assign students to critiquing AI-generated answers.
    • Projects require creative input – not just output.
    • Assessment values reasoning not memorization
    • Ethics and digital responsibility are formally taught.

    In such environments:

    • Students think first,
    • AI helps second
    • Learning is deeply human.

    5. When AI Becomes a Bane

    AI becomes harmful when:

    • It replaces effort instead of supporting it.
    • It is used secretly, not transparently.
    • Exams test outdated memorization skills.
    • Teachers are not trained to integrate it meaningfully.
    • Institutions chase efficiency at the cost of depth.

    In these cases:

    • Discipline is replaced by dependency.
    • Convenience replaces curiosity.
    • Output replaces understanding.

    6. The Question Is Not “Boon or Bane”It Is “What Kind of Education Do We Want?”

    AI is making education systems confront a deeper issue they have long postponed:

    • Do we want our students to recall information?
    • Or students who analyze, create, and judge wisely?

    Memorization-based education is going obsolete-not because AI is evil, but because the world no longer pays for recall alone. A future belongs to:

    • Critical thinkers
    • Ethical Users of Technology
    • Creative problem solvers
    • lifelong learners

    If education evolves in this direction, AI turns into a historic boon.

    If it does not, then AI becomes a silent destroyer of depth.

    7. Final Balanced Conclusion

    So, is AI a boon or a bane for education?

    It is a boon for:

    • Personalization
    • Access
    • Speed of learning
    • Teacher Empowerment
    • Global knowledge sharing

    It becomes a bane for:

    • Deep thinking
    • Authentic creativity
    • Assessment integrity
    • Human intellectual ownership
    • Equity when access is uneven

    The Real Answer

    AI is neither a savior nor a villain.

    It is a mirror reflecting the priorities, values, and wisdom of the education systems using it.

    If we center education on:

    • Thought, not shortcuts
    • Understanding, not output
    • Growth not grades

    Then AI becomes one of the greatest educational tools humanity has ever created.

    Designing education around the following: Speed over depth Convenience over character Results over reasoning Then AI will weaken the very foundation of learning.

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mohdanasMost Helpful
Asked: 09/12/2025In:

creativity, critical thinking, and ac ...

academic integrityai in educationcreativitycritical thinkingedtechstudent outcomes
  1. mohdanas
    mohdanas Most Helpful

    1. How AI Is Genuinely Improving Student Outcomes Personalized Learning at Scale For the first time in history, education can adapt to each learner in real time. AI systems analyze how fast a student learns, where they struggle, and what style works best. A slow learner gets more practice; a fast leRead more

    1. How AI Is Genuinely Improving Student Outcomes

    Personalized Learning at Scale

    For the first time in history, education can adapt to each learner in real time.

    • AI systems analyze how fast a student learns, where they struggle, and what style works best.

    • A slow learner gets more practice; a fast learner moves ahead instead of feeling bored.

    • This reduces frustration, dropout rates, and academic anxiety.

    In traditional classrooms, one teacher must design for 30 50 students at once. AI allows one-to-one digital tutoring at scale, which was previously impossible.

    Instant Feedback = Faster Learning

    Students no longer need to wait days or weeks for evaluation.

    • AI can instantly assess essays, coding assignments, math problems, and quizzes.

    • Immediate feedback shortens the learning loop—students correct mistakes while the concept is still fresh.

    • This tight feedback cycle significantly improves retention.

    In learning science, speed of feedback is one of the strongest predictors of improvement AI excels at this.

    Accessibility & Inclusion

    AI dramatically levels the playing field:

    • Speech-to-text and text-to-speech for students with disabilities

    • Language translation for non-native speakers

    • Adaptive pacing for neurodiverse learners

    • Affordable tutoring for students who cannot pay for private coaching

    For millions of students worldwide, AI is not a luxury it is their first real access to personalized education.

    Teachers Gain Time for Meaningful Teaching

    Instead of spending hours on:

    • Grading

    • Attendance

    • Quiz creation

    • Administrative paperwork

    Teachers can focus on:

    • Mentorship

    • Discussion

    • Higher-order thinking

    • Emotional and motivational support

    When used well, AI doesn’t replace teachers, it upgrades their role.

    2. The Real Risks: Creativity, Critical Thinking & Integrity

    Now to the other side, which is just as serious.

    Risk to Creativity: “Why Think When AI Thinks for You?”

    Creativity grows through:

    • Struggle

    • Exploration

    • Trial and error

    • Original synthesis

    If students rely on AI to:

    • Write essays

    • Design projects

    • Generate ideas instantly

    Then they may consume creativity instead of developing it.

    Over time, students may become:

    • Good at prompting

    • Poor at imagining

    • Skilled at editing

    • Weak at originality

    Creativity weakens when the cognitive struggle disappears.

    Risk to Critical Thinking: Shallow Understanding

    Critical thinking requires:

    • Questioning

    • Argumentation

    • Evaluation of evidence

    • Logical reasoning

    If AI becomes:

    • The default answer generator

    • The shortcut instead of the thinking process

    Then students may:

    • Memorize outputs without understanding logic

    • Accept answers without verification

    • Lose patience for deep reasoning

    This creates surface learners instead of analytical thinkers.

    Academic Integrity: The Trust Crisis

    This is currently the most visible risk.

    • AI-written essays are difficult to detect.

    • Code generated by AI blurs authorship.

    • Homework, reports, even exams can be auto-generated.

    This leads to:

    • Credential dilution (“Does this degree actually prove skill?”)

    • Unfair advantages

    • Loss of trust between teachers and students

    Education systems are now facing an integrity arms race between AI generation and AI detection.

    3. The Core Truth: AI Is a Cognitive Amplifier, Not a Moral Agent

    AI does not:

    • Teach values

    • Build character

    • Develop curiosity

    • Instill discipline

    It only amplifies what already exists in the learner.

    • A motivated student becomes faster and sharper.

    • A disengaged student becomes more dependent and passive.

    So the outcome depends less on AI itself and more on:

    • How students are trained to use it

    • How teachers structure learning around it

    • How institutions define assessment and accountability

    4. When AI Strengthens Creativity & Thinking (Best-Case Use)

    AI improves creativity and reasoning when it is used as a thinking partner, not a replacement.

    Good examples:

    • Students generate their own ideas first, then refine with AI

    • AI provides alternative viewpoints for debate

    • Students critique AI-generated answers for accuracy and bias

    • AI is used for simulations, not final conclusions

    In this model:

    • Human thinking stays primary

    • AI becomes a cognitive accelerator

    This leads to:

    • Deeper exploration

    • More experimentation

    • Higher creative output

    5. When AI Undermines Learning (Worst-Case Use)

    AI becomes harmful when it is used as a thinking substitute:

    • “Write my assignment.”

    • “Solve this exam question.”

    • “Generate my project idea.”

    • “Make my presentation.”

    Here:

    • Learning becomes transactional

    • Effort collapses

    • Understanding weakens

    • Credentials lose meaning

    This is not a future risk it is already happening in many institutions.

    6. The Future Will Demand New Skills, Not No Skills

    Ironically, AI does not reduce the need for human thinking it raises the bar for what humans must be good at:

    Future-proof skills include:

    • Critical reasoning

    • Ethical judgment

    • Systems thinking

    • Emotional intelligence

    • Creativity and design thinking

    • Problem framing (not just problem solving)

    Education systems that continue to test:

    • Memorization

    • Formulaic writing

    • Repetitive problem solving

    Will become outdated in the AI era.

    7. Final Balanced Answer

    Does AI-driven learning improve outcomes?
    Yes.

    • It personalizes education.

    • It accelerates learning.

    • It expands access.

    • It reduces administrative burdens.

    • It improves skill acquisition.

    Does it risk undermining creativity, critical thinking, and integrity?
    Also yes.

    • If used as a shortcut instead of a scaffold.

    • If assessment systems stay outdated.

    • If students are not trained in ethical use.

    • If originality is no longer rewarded.

    The Real Conclusion

    AI will not make students smarter or dumber by itself.
    It will make visible what education systems truly value.

    If we reward:

    • Speed over depth → we get shallow learning.

    • Output over understanding → we get dependency.

    • Grades over growth → we get academic dishonesty.

    But if we redesign education around:

    • Thinking, not typing

    • Reasoning, not regurgitation

    • Creation, not copying

    Then AI becomes one of the most powerful educational tools ever created.

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daniyasiddiquiEditor’s Choice
Asked: 25/11/2025In:

generative-AI tools be integrated int ...

ai in educationeducational technologygenerative ai toolsresponsible ai useteacher augmentationteaching enhancement
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    How generative-AI can augment rather than replace educators Generative AI is reshaping education, but the strongest emerging consensus is that teaching is fundamentally relational. Students learn best when empathy, mentorship, and human judgment remain at the core. AI should therefore operate as a cRead more

    How generative-AI can augment rather than replace educators

    Generative AI is reshaping education, but the strongest emerging consensus is that teaching is fundamentally relational. Students learn best when empathy, mentorship, and human judgment remain at the core. AI should therefore operate as a co-pilot, extending teachers’ capabilities, not substituting them.

    The key is to integrate AI into workflows in a way that enhances human strengths (creativity, mentoring, contextual decision-making) and minimizes human burdens (repetitive tasks, paperwork, low-value administrative work).

    Below are the major ways this can be done practical, concrete, and grounded in real classrooms.

    1. Offloading routine tasks so teachers have more time to teach

    Most teachers lose up to 30–40 percent of their time to administrative load. Generative-AI can automate parts of this workload:

    Where AI helps:

    • Drafting lesson plans, rubrics, worksheets

    • Creating differentiated versions of the same lesson (beginner/intermediate/advanced)

    • Generating practice questions, quizzes, and summaries

    • Automating attendance notes, parent communication drafts, and feedback templates

    • Preparing visual aids, slide decks, and short explainer videos

    Why this augments rather than replaces

    None of these tasks define the “soul” of teaching. They are support tasks.
    By automating them, teachers reclaim time for what humans do uniquely well coaching, mentoring, motivating, dealing with individual student needs, and building classroom culture.

    2. Personalizing learning without losing human oversight

    AI can adjust content level, pace, and style for each learner in seconds. Teachers simply cannot scale personalised instruction to 30+ students manually.

    AI-enabled support

    • Tailored explanations for a struggling student

    • Additional challenges for advanced learners

    • Adaptive reading passages

    • Customized revision materials

    Role of the teacher

    The teacher remains the architect choosing what is appropriate, culturally relevant, and aligned with curriculum outcomes.
    AI becomes a recommendation engine; the human remains the decision-maker and supervisor for quality, validity, and ethical use.

    3. Using AI as a “thought partner” to enhance creativity

    Generative-AI can amplify teachers’ creativity:

    • Suggesting new teaching strategies

    • Producing classroom activities inspired by real-world scenarios

    • Offering varied examples, analogies, and storytelling supports

    • Helping design interdisciplinary projects

    Teachers still select, refine, contextualize, and personalize the content for their students.

    This evolves the teacher into a learning designer, supported by an AI co-creator.

    4. Strengthening formative feedback cycles

    Feedback is one of the strongest drivers of student growth but one of the most time-consuming.

    AI can:

    • Provide immediate, formative suggestions on drafts

    • Highlight patterns of errors

    • Offer model solutions or alternative approaches

    • Help students iterate before the teacher reviews the final version

    Role of the educator

    Teachers still provide the deep feedback the motivational nudges, conceptual clarifications, and personalised guidance AI cannot replicate.
    AI handles the low-level corrections; humans handle the meaningful interpretation.

    5. Supporting inclusive education

    Generative-AI can foster equity by accommodating learners with diverse needs:

    • Text-to-speech and speech-to-text

    • Simplified reading versions for struggling readers

    • Visual explanations for neurodivergent learners

    • Language translation for multilingual classrooms

    • Assistive supports for disabilities

    The teacher’s role is to ensure these tools are used responsibly and sensitively.

    6. Enhancing teachers’ professional growth

    Teachers can use AI as a continuous learning assistant:

    • Quickly understanding new concepts or technologies

    • Learning pedagogical methods

    • Getting real-time answers while designing lessons

    • Reflecting on classroom strategies

    • Simulating difficult classroom scenarios for practice

    AI becomes part of the teacher’s professional development ecosystem.

    7. Enabling data-driven insights without reducing students to data points

    Generative-AI can analyze patterns in:

    • Class performance

    • Engagement trends

    • Topic-level weaknesses

    • Behavioral indicators

    • Assessment analytics

    Teachers remain responsible for ethical interpretation, making sure decisions are humane, fair, and context-aware.
    AI identifies patterns; the teacher supplies the wisdom.

    8. Building AI literacy and co-learning with students

    One of the most empowering shifts is when teachers and students learn with AI together:

    • Discussing strengths/limitations of AI-generated output

    • Evaluating reliability, bias, and accuracy

    • Debating ethical scenarios

    • Co-editing drafts produced by AI

    This positions the teacher not as someone to be replaced, but as a guide and facilitator helping students navigate a world where AI is ubiquitous.

    The key principle: AI does the scalable work; the teacher does the human work

    Generative-AI excels at:

    • Scale

    • Speed

    • Repetition

    • Pattern recognition

    • Idea generation

    • Administrative support

    Teachers excel at:

    • Empathy

    • Judgment

    • Motivation

    • Ethical reasoning

    • Cultural relevance

    • Social-emotional development

    When systems are designed correctly, the two complement each other rather than conflict.

    Final perspective

    AI will not replace teachers.

    But teachers who use AI strategically will reshape education.

    The future classroom is not AI-driven; it is human-driven with AI-enabled enhancement.

    The goal is not automation it is transformation: freeing educators to do the deeply human work that machines cannot replicate.

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daniyasiddiquiEditor’s Choice
Asked: 15/10/2025In: ,

design assessments in the age of AI

academic integrityai in educationassessment designauthentic assessmentedtechfuture of assessment
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    How to Design Tests in the Age of AI In this era of learning, everything has changed — not only the manner in which students learn but also the manner in which they prove that they have learned. Students today employ tools such as ChatGPT, Grammarly, or math solution AI tools as an integral part ofRead more

    How to Design Tests in the Age of AI

    In this era of learning, everything has changed — not only the manner in which students learn but also the manner in which they prove that they have learned. Students today employ tools such as ChatGPT, Grammarly, or math solution AI tools as an integral part of their daily chores. While technology enables learning, it also renders the conventional models of assessment through memorization, essays, or homework monotonous.

    So the challenge that educators today are facing is:

    How do we create fair, substantial, and authentic tests in a world where AI can spew up “perfect” answers in seconds?

    The solution isn’t to prohibit AI — it’s to redefine the assessment process itself. Let’s start on how.

    1. Redefining What We’re Assessing

    For generations, education has questioned students about what they know — formulas, facts, definitions. But machines can memorize anything at the blink of an eye, so tests based on memorization are becoming increasingly irrelevant.

    In the AI era, we must test what AI does not do well:

    • Critical thinking — Do students understand AI-presents information?
    • Creativity — Can they leverage AI as a tool to make new things?
    • Ethical thinking — Do they know when and how to apply AI in an ethical manner?
    • Problem setting — Can they establish a problem first before looking for a solution?

    Attempt replacing the following questions: Rather than asking “Explain causes of World War I,” ask “If AI composed an essay on WWI causes, how would you analyze its argument or position?”

    This shifts the attention away from memorization.

     2. Creating “AI-Resilient” Tests

    An AI-resilient assessment is one where even if a student uses AI, the tool can’t fully answer the question — because the task requires human judgment, personal context, or live reasoning.

    Here are a few effective formats:

    • Oral and interactive assessments:Ask students to explain their thought process verbally. You’ll see instantly if they understand the concept or just relied on AI.
    •  Process-based assessment:Rather than grading the final product alone, grade the process — brainstorm, drafts, feedback, revisions.

    Have students record how they utilized AI tools ethically (e.g., “I used AI to grammar-check but wrote the analysis myself”).

    •  Scenario or situational activities:Provide real-world dilemmas that need interpretation, empathy, and ethical thinking — areas where AI is not yet there.

    Choose students for the competition based on how many tasks they have been able to accomplish.

    Example: “You are an instructor in a heterogeneously structured class. How do you use AI in helping learners of various backgrounds without infusing bias?”

    Thinking activities:

    Instruct students to compare or criticize AI responses with their own ideas. This compels students to think about thinking — an important metacognition activity.

     3. Designing Tests “AI-Inclusive” Not “AI-Proof”

    it’s a futile exercise trying to make everything “AI-proof.” Students will always find new methods of using the tools. What needs to happen instead is that tests need to accept AI as part of the process.

    • Teach AI literacy: Demonstrate how to use AI to research, summarize, or brainstorm — responsibly.
    • Request disclosure: Have students report when and how they utilized AI. It encourages honesty and introspection.

    Mark not only the result, but their thought process as well: Have students discuss why they accepted or rejected AI suggestions.

    Example prompt:

    • “Use AI to create three possible solutions to this problem. Then critique them and let me know which one you would use and why.”

    This makes AI a study buddy, and not a cheat code.

     4. Immersing Technology with Human Touch

    Teachers should not be driven away from students by AI — but drawn closer by making assessment more human-friendly and participatory.

    Ideas:

    • Blend virtual portfolios (AI-written writing, programmed coding, or designed design) with face-to-face discussion of the student’s process.
    • Tap into peer review sessions — students critique each other’s work, with human judgment set against AI-produced output.
    • Mix live, interactive quizzes — in which the questions change depending on what students answer, so the tests are lifelike and surprising.

    Human element: A student may use AI to redo his report, but a live presentation tells him how deep he really is.

     5. Justice and Integrity

    Academic integrity in the age of AI is novel. Cheating isn’t plagiarizing anymore but using crutches too much without comprehending them.

    Teachers can promote equity by:

    • Having clear AI policies: Establishing what is acceptable (e.g., grammar assistance) and not acceptable (e.g., writing entire essays).

    Employing AI-detecting software responsibly — not to sanction, but to encourage an open discussion.

    • Requesting reflection statements: “Tell us how you employed AI on the completion of this assignment.”

    It builds trust, not fear, and shows teachers care more about effort and integrity than being great.

     6. Remixing Feedback in the AI Era

    • AI can speed up grading, but feedback must be human. Students learn optimally when feedback is personal, empathetic, and constructive.
    • Teachers can use AI to produce first-draft feedback reports, then revise with empathy and personal insight.
    • Have students use AI to edit their work — but ask them to explain what they learned from the process.
    • Focus on growth feedback — learning skills, not grades.

     Example: Instead of a “AI plagiarism detected” alert, give a “Let’s discuss how you can responsibly use AI to enhance your writing instead of replacing it.” message.

     7. From Testing to Learning

    The most powerful change can be this one:

    • Testing no longer has to be a judgment — it can be an odyssey.

    AI eliminates the myth that tests are the sole measure of demonstrating what is learned. Tests, instead, become an act of self-discovery and learning skills.

    Teachers can:

    • Substitute high-stakes testing with continuous formative assessment.
    • Incentivize creativity, critical thinking, and ethical use of AI.
    • Students, rather than dreading AI, learn from it.

    Final Thought

    • The era of AI is not the end of actual learning — it’s the start of a new era of testing.
    • A time when students won’t be tested on what they’ve memorized, but how they think, question, and create.
    • An era where teachers are mentors and artists, leading students through a virtual world with sense and sensibility.
    • When exams encourage curiosity rather than relevance, thinking rather than repetition, judgment rather than imitation — then AI is not the enemy but the ally.

    Not to be smarter than AI. To make students smarter, more moral, and more human in a world of AI.

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daniyasiddiquiEditor’s Choice
Asked: 15/10/2025In: ,

AI assist rather than replace teacher

ai in educationclassroom innovationedtecheducaion technologyhuman-ai collaborationteacher support
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    What can the AI do instead of replacing teachers? The advent of Artificial Intelligence (AI) in education has sparked both excitement and fear. Teachers wonder — will AI replace teachers? But the truth is, AI has its greatest potential not in replacing human teachers, but assisting them. When used sRead more

    What can the AI do instead of replacing teachers?

    The advent of Artificial Intelligence (AI) in education has sparked both excitement and fear. Teachers wonder — will AI replace teachers? But the truth is, AI has its greatest potential not in replacing human teachers, but assisting them. When used strategically, AI can make teachers more effective, more customized, and more creative in their work, so that they can focus on the things computers can’t do — empathy, motivation, and relating to individuals.

    Let us observe how AI can assist rather than substitute teachers in the new classrooms of today.

     1. Personalized Instruction for All Pupils

    • Every pupil has a distinct learning style — some learn fast, while others need more time or instructions. With AI, teachers can know such differences in learning in real time.
    • Adaptive learning software reviews the way in which students interact with content — how long on a question, what they get wrong, or what they’re having difficulty with.
    • Based on that, the system slows down or suggests more practice.
    • For instance, AI systems like Khanmigo (the artificial intelligence tutor from Khan Academy) or Century Tech allow teachers to track individual progress and view who needs additional support or challenge.

     Human edge: Educators then use this data to guide interventions, provide emotional support, or adjust strategy — stuff AI doesn’t understand or feel.

    2. Reducing Administrative Tasks

    Teachers waste their time grading assignments, creating materials, or composing reports — activities that steal time from teaching.

    AI can handle the drudgework:

    • Grading assistance: AI automatically grades objective tests (e.g., multiple choice or short answer).
    • Lesson planning: AI apps can create sample lesson plans or quizzes for a topic or skill.
    • Progress tracking: AI dashboards roll together attendance, grades, and progress in learning, so instructors can focus on strategy and not spreadsheets.
    • Teacher benefit: Saving paperwork time, instructors have more one-on-one time with students — listening, advising, and encouraging inquiry.

     3. Differentiated Instruction Facilitation

    • In a single classroom, there can be advanced students, average students, and struggling students with basic skills. AI can offer differentiated instruction automatically by offering customized materials to every learner.
    • For example, AI can recommend reading passages of different difficulty levels but on a related topic to ensure all of them contribute to class discussions.
    • For language learning, AI is able to personalize practice exercises in pronunciation or grammar practice to the level of fluency of the student.

     Human benefit: Teachers are able to use these learnings to put students in groups so they can learn from each other, get group assignments, or deliver one-on-one instruction where necessary.

     4. Overcoming Language and Accessibility Barriers

    • Artificially intelligent speech recognition and translation software (e.g., Microsoft’s Immersive Reader or Google’s Live Transcribe) aid multilingual or special-needs students to fully participate in class.
    • Text-to-speech and speech-to-text software helps hearing loss or dyslexia students.
    • AI translation allows non-native speakers to hear classes in real-time.

     Human strength: Educators are still the bridge — not only translating words, but also context, tone, and feeling — and making it work for inclusion and belonging.

    5. Data-Driven Insights for Better Teaching

    • Computer systems can look across patterns of learning over the course of a class — perhaps seeing that the majority of students had trouble with a certain concept. Teachers can then respond promptly by adjusting lessons or re-teaching to stop misunderstandings from spreading.
    • AI doesn’t return grades — it returns patterns.
    • Teachers can use them to guide teaching approach, pace, and even classroom layout.

    Human edge: AI gives us data, but only educators can take that and turn it into knowledge — when to hold, when to move forward, and when to just stop and talk.

     6. Innovative Co-Teaching Collaborator

    • AI can serve as a creative brainstorming collaborator for instructors.
    • Generative AI (Google Gemini or ChatGPT) can be leveraged by educators to come up with examples, analogies, or ideas for a project within seconds.
    • AI can replicate debate opponents or generate practice essays for class testing.

    Human strength: Teachers infuse learning with imagination, moral understanding, and a sense of humor — all out of the reach of algorithms.

     7. Emotional Intelligence and Mentorship — The Human Core

    • The most significant difference, perhaps, is this one: AI lacks empathy. It can simulate feeling in voice or words but never feels compassion, enthusiasm, or concern.
    • Teachers don’t just teach facts — they also give confidence, character, and curiosity. They notice when a child looks blue, when a student is off task, or when a class needs to laugh at more than one more worksheet.

    AI can’t replace that. But it can amplify it — releasing teachers from soul-crushing drudgery and giving them real-time feedback, it allows them to remain laser-sharp on what matters most: being human with children.

    8. The Right Balance: Human–AI Collaboration

    The optimal classroom of the future will likely be hybrid — where data, repetition, and adaptation are handled by AI, but conversation, empathy, and imagination are crafted by teachers.

    In balance:

    • AI is a tool, and not an educator.
    • Teachers are designers of learning, utilizing AI as a clever assistant, and not a competitor.

     Last Thought

    • AI does not substitute for teachers; it needs them.
    • Without the hand of a human to steer it, AI can be biased, uninformed, or emotionally numb.
    • But with a teacher in charge, AI is a force multiplier — enabling each student to learn more effectively, more efficiently, and more profoundly.

    AI shouldn’t be replacing the teacher in the classroom. It needs to make the teacher more human — less.

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daniyasiddiquiEditor’s Choice
Asked: 25/09/2025In: ,

assessment and feedback

ai in educationai-feedbackedtechlanguage-assessmentlanguage-learningpersonalized-learning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     The Timeless Problem with Learning Language Language learning is intimate, but traditional testing just can't manage that. Students are typically assessed by rigid, mass-produced methods: standardized testing, fill-in-the-blank, checklist-graded essays, etc. Feedback can be delayed for days, frequeRead more

     The Timeless Problem with Learning Language

    Language learning is intimate, but traditional testing just can’t manage that. Students are typically assessed by rigid, mass-produced methods: standardized testing, fill-in-the-blank, checklist-graded essays, etc. Feedback can be delayed for days, frequently in the form of generic comments like “Good job!” or “Elaborate on your points.” There’s little nuance. Little context. Little you engaged.

    That’s where AI comes in—not to do the teachers’ job, but as a super-competent co-pilot.

     AI/LLMs Change the Game

    1. Measuring Adapted Skills

    • AI models can examine a learner’s language skills in real time, in listening, reading, writing, and even speech (if integrated with voice systems). For example:
    • As a learner writes a paragraph, my LLM can pass judgment on grammar, vocabulary richness, coherence, tone, and argument strength.
    • Instead of just giving a score, it can explain why a sentence may be unclear or how a certain word choice could be improved.
    • Over time, the model can track the learner’s progress, detect plateaus, and suggest focused exercises.

    It’s not just feedback—it’s insight.

    2. Personalized Feedback in Natural Language

    Instead of “Incorrect. Try again,” an AI can say:

    “‘You’re giving ‘advices’ as a plural, but ‘advice’ is an uncountable noun in English. You can say ‘some advice’ or ‘a piece of advice.’ Don’t worry—this is a super common error.'”

    This kind of friendly, particular, and human feedback promotes confidence, not nervousness. It’s immediate. It’s friendly. And it makes learners feel seen.

    3. Shifting to Level of Proficiency and Learning Style

    AI systems are able to adjust the level and tone of their feedback to meet the learner’s level:

    • For beginning learners: shorter, more direct explanations; focus on basic grammar and sentence structure.
    • For advanced learners: feedback might include stylistic remarks, rhetorical impact, tone modulations, and even cultural context.

    It also has the ability to understand how the individual learns best: visually, by example, by analogy, or by step-by-step instructions. Think of receiving feedback described in the mode of a story or in the way of colored correction, depending on your preference.

    4. Multilingual Feedback and Translation Support

    For multilingual students or ESL, AI can specify errors in the student’s home language, compare the structures of different languages, and even flag “false friends” (i.e., words that are the same but have different meanings in two languages).

    • “In Spanish, ’embarazada’ means pregnant—not embarrassed! Easy mix-up.”
    • That’s the type of contextual foundation that makes feedback sticky.

    5. Real-Time Conversational Practice

    With the likes of voice input and chat interfaces, LLMs can practice real-life conversations:

    • Job interview, travel scenario, or conversation practice course.
    • Giving feedback on your pronunciation, tone, or idiomatic usage.
    • Even role-reversal (e.g., “pretend that I were a traveler in Japan”) to get used to different contexts.

    And the best part? No judgment. You can make mistakes without blushing.

    6. Content Generation for Assessment

    Teachers or students may ask AI to create custom exercises based on a provided topic or difficulty level: teaching

    • Fill-in-blank exercises based on vocabulary from a recent lesson.
    • Comprehension questions based on a passage the learner wrote.
    • Essay prompts based on student interests (“Write about your favorite anime character in past tense.”)
    • This makes assessment more engaging—and more significant.

     Why This Matters: Personalized Learning Is Powerful Learning

    Language learning is not a straight line. Others struggle with verb conjugation, others with pronunciation or cultural uses of language. Others get speech-tongue-tied, others are grammar sticklers who can’t write a wonderful sentence.

    LLMs are able to identify such patterns, retain preferences (with permission), and customize not only feedback, but the entire learning process. Picture having a tutor who daily adjusts to your changing needs, is on call 24/7, never gets fatigued, and pumps you up each step of the way.

    That’s the magic of customized AI.

    Of Course, It’s Not Perfect

    • Come on, let’s be realistic—AI has its limits.
    • It will sometimes fail to pick up subtleties of meaning or tone.
    • Feedback at times was too pleasant, or not harsh.
    • It also lacks cultural awareness or emotional intelligence in edge cases.

    And let’s not forget the risk of students becoming too reliant on AI tools, instead of learning to think by themselves.

    That’s why human teachers matter more than ever before. The optimal model is AI-assisted learning: teachers + AI, not teachers vs. AI.

    What’s Next?

    The future may bring:

    • LLMs tracking a student’s work such as an electronic portfolio.
    • AI with voice recognition utilized in the assessment of speaking fluency.
    • AI grading lengthy essays with feedback that is written in a tone in which one would speak.

    Even writing partners who help you co-author tales and revise and explain along the way.

     Final Thought

    Personalized language assessment with LLMs isn’t a matter of time-saving or feedbackscaling—it’s a matter of giving the learner a sense of having been heard. Inspired. Empowered. When a student is informed, “I see what you’re attempting to say—here’s how to say it better,” that’s when real growth happens.

    And if AI can make that experience more available, more equitable, and more inspiring for millions of learners across the globe—well, that’s a very good application of intelligence.

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mohdanasMost Helpful
Asked: 22/09/2025In:

critical thinking, creativity, digita ...

ai challengesai in educationai toolsdigital literacyedtecheducation policystudent learning
  1. mohdanas
    mohdanas Most Helpful

    The Future Isn't Just About Jobs, It's About Adaptability In a world ruled by AI, the greatest change is not so much what kind of jobs there are but how rapidly they shift. Occupations that were rock-solid for decades can become obsolete in a few short years. That means students don't merely need toRead more

    The Future Isn’t Just About Jobs, It’s About Adaptability

    In a world ruled by AI, the greatest change is not so much what kind of jobs there are but how rapidly they shift. Occupations that were rock-solid for decades can become obsolete in a few short years. That means students don’t merely need to train for one job—they need the flexibility to learn, unlearn, and remake themselves over their lifetime.

    So the question is: which abilities will maintain their worth, as industries change and automation becomes more widespread?

    1. Critical Thinking – The Compass in a World of Noise

    AI can provide answers in seconds, but it doesn’t always provide good answers. Students will need the capacity to question, validate, and think through information. Critical thinking is the ability that allows you to distinguish fact from fiction, logic from prejudice, insight from noise.

    Envision a future workplace: an AI generates a business plan or science report. A seasoned professional won’t merely take it—they’ll question: Does this hold together? What’s omitted? What’s the implicit assumption? That critical thinking skill will be a student’s protection against uncritically adopting machine outputs.

    2. Creativity – The Human Edge Machines Struggle With

    Whereas machines may create art, code, or even music, they typically take from what already exists. Creativity lies in bridging ideas between fields, posing “What if?” questions, and being brave enough to venture into the unknown.

    Future professions—be they in design, engineering, medicine, or business—will require human beings who can envision possibilities that AI has not “seen” yet. Creativity is not only for painters; it’s for anyone who invents solutions in new ways.

    3. Digital Literacy – Adapting to the Language of AI

    As reading and math literacy became a way of life, digital literacy will be a requirement. Students won’t have to be master programmers, but they will need to comprehend the mechanisms of AI systems, their boundaries, and their moral issues.

    Just like learning to drive in a car-filled world: you don’t have to be a mechanic, but you need to understand the rules of the road. Graduating students ought to feel assured in applying AI tools ethically, and be aware of how data and algorithms influence the world.

    4. Emotional Intelligence – The “Human Glue” of Workplaces

    While machines assume repetitive and technical work, the uniquely human abilities of empathy, teamwork, and communication gain greater value. Emotional intelligence (EQ) is what enables individuals to deal with relationships, mediate conflicts, and lead with empathy.

    The workplaces of the future will depend hugely on collaboration between humans and AI, but also between humans. Individuals who are able to see from others’ points of view, inspire teams, and establish trust will be highly valued, regardless of industry.

    5. Adaptability & Lifelong Learning – The Skill. Under All Skills

    The reality is, however much schools may attempt, they cannot forecast. perfectly which specific hard skills will reign in 20 years. What they can provide is the mind. set. of learning itself—curiosity, tenacity, and flexibility.

    Students who recognize change not as a threat but as opportunity will be successful. They’ll reskill, explore new areas, keep up with technology rather than hating it. In many respects, the disposition of lifelong learning is more crucial than the acquisition of any one technical skill.

    Beyond the “Big Four”: Other Emerging Skills

    • Ethical reasoning → informing how AI and tech should be used responsibly.
    • Cross-cultural collaboration → operating in a globalized, remote, multicultural setting.
    • Storytelling & communication → being able to make difficult concepts clear and compelling.

    The Bigger Picture: Education Needs to Catch Up

    Schools tend to still follow 20th-century models—memorization, the standardized test, and rigid subject silos. But the world of AI requires a transition to interdisciplinary projects, real-world problem-solving, and room for creativity. It is not a matter of adding more into the curriculum, but reframing what it is to “be educated.”

    Briefly: the most prized skills will be those that make humans remain irreplaceable—critical thinking, creativity, digital literacy, and emotional intelligence—coupled with adaptability and lifelong learning. If students develop these, they’ll be prepared not only for the next job market, but for the next few.

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mohdanasMost Helpful
Asked: 22/09/2025In: ,

AI tools that help students learn ver ...

ai in educationai toolsclassroom technologyeducational technologystudent engagementstudent learning
  1. mohdanas
    mohdanas Most Helpful

    The Double-Edged Sword of AI in Education AI in the classroom feels very much like providing every student with his or her own personal tutor—except that it also, when abused, will simply provide the answers. On the positive side, these technologies can unleash personalized learning, provide immediaRead more

    The Double-Edged Sword of AI in Education

    AI in the classroom feels very much like providing every student with his or her own personal tutor—except that it also, when abused, will simply provide the answers. On the positive side, these technologies can unleash personalized learning, provide immediate feedback, and even allow students to master difficult concepts in ways that even the best teachers cannot. On the other hand, they create prima facie concerns: students could forego the thought process altogether and use AI-provided answers, or incorporate them to plagiarize essays and assignments.

    The equilibrium schools must find isn’t one of prohibiting AI and the other of opening the arms to it—it’s one of regulating how it’s employed.

    Changing the Mindset from “Cheating” to “Learning Aid”

    Consider the calculators in mathematics education. When they first emerged, educators feared they would kill students’ ability to perform arithmetic. Now, we don’t debate whether or not to ban calculators—instead, we instruct on how and when to use them. The same philosophy should be applied to AI. If students are educated to know that AI isn’t there to get the job done for them but to better comprehend, it’s less about shortcuts and more about building skill.

    Teaching AI Literacy Alongside Subject Knowledge

    One practical solution is to actually teach students how AI works, where it’s strong, and where it fails. By learning to question AI outputs, students develop both digital literacy and critical thinking. For example:

    • A history teacher could ask students to fact-check an AI-generated essay for accuracy.
    • A science teacher could have students use AI to brainstorm hypotheses, but then require evidence-based testing in class.

    This manner, AI becomes integral to the lesson instead of an exploit.

    Assessment Must Adapt

    Another wake-up call: if we continue to rely on standard homework essays or take-home tests as the primary tools for assessment, AI will forever be an invitation. Schools may need to reinvent assessments to place greater emphasis on:

    • In-class projects that demonstrate genuine comprehension.
    • Oral debates and presentations, where students describe concepts in their own words.
    • Challenge problems that lie beyond an AI’s neatly generated capabilities.

    It doesn’t mean homework vanishes—it just means we reimagine what we have students work on at home versus in class.

    Teachers as Guides, Not Gatekeepers

    The teacher’s role becomes less policing and more mentoring. A teacher could say: “Yes, you can use AI to come up with ideas for your essay—but you have to let me see your process, tell me why you accepted or discarded some of the suggestions, and you have to contribute your own original ideas.” That openness makes it less easy for students to cheat behind AI but still enables them to take advantage of it.

    Preparing Students for the Real World

    Maybe the best reason to include AI responsibly is that, outside school, AI will permeate everywhere—offices, labs, creative sectors, even daily life. Schools owe it to their students not to protect them from AI, but to prepare them to employ it morally and efficiently. That involves teaching boundaries: when it’s acceptable to rely on AI (such as summarizing complex text), and when it stifles development (such as copying an entire essay).

    The Human Core Still Matters

    Fundamentally, education is not just about obtaining the “right answer.” It’s about cultivating curiosity, grit, and independent thought. AI is a mighty tool, but it must never substitute for human qualities. The challenge—and opportunity—of this moment is to make AI an enabling partner, not a crutch.

    Briefly: Balance is integration with purpose. Rather than dreading AI as learning’s enemy, schools can make it an ally in teaching, and reshape tests and expectations so that learners continue to develop their own voices and thinking skills.

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