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daniyasiddiquiEditor’s Choice
Asked: 17/09/2025In: Education, News, Technology

How to assess deeper learning, critical thinking, creativity rather than rote or recall?

deeper learning, critical thinking, c ...

creativethinkingcriticalthinkingdeeperlearningmetacognitionprojectbasedlearning
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice
    Added an answer on 17/09/2025 at 4:03 pm

    Why Old-Fashioned Tests Come Up Short Assignments and tests were built on the model of recall for years: reciting definitions, remembering dates from history, calculating standard math problems. These were easy to grade and standardize. But the danger is self-evident: a pupil can memorize just enougRead more

    Why Old-Fashioned Tests Come Up Short

    Assignments and tests were built on the model of recall for years: reciting definitions, remembering dates from history, calculating standard math problems. These were easy to grade and standardize. But the danger is self-evident: a pupil can memorize just enough to get through a test but exit without true understanding. Worse, they can “forget” everything in weeks.

    If we only measure what can be memorized, we are likely to reward short-term cramming instead of lifelong learning. And with all the AI around us, remembering is no longer the key skill.

    What Deeper Learning Looks Like

    Deeper learning is *transfer*—the capacity to apply knowledge to *new, unfamiliar* contexts. It takes the form of:

    • Critical thinking: Asking “why,” examining sources, challenging assumptions.
    • Creativity: Coming up with new ideas, seeing connections between subjects.
    • Problem-solving: Applying concepts in creative ways to understand actual situations.
    • Collaboration: Standing on one another’s shoulders, figuring out meaning collaboratively.
    • Self-reflection: Knowing one’s own strengths, weaknesses, and areas of improvement.

    The question is: how do we measure these?

    1. Open-Ended Performance Tasks

    Rather than multiple-choice, give students messy problems with no single best solution.

    • Example: Replace “What caused the French Revolution?” with “If you were a political leader in 1789, what reforms would you suggest to avoid revolution, and why?

    In this way, the student is asked to synthesize information, reconcile perspectives, and justify choices—thinking, not recalling.

     2. Portfolios & Iterative Work

    One essay illustrates a final product, but not the learning process. Portfolios allow students to illustrate drafts, revisions, reflections, and growth.

    • Example: A student of art submits sketches, experiments, mistakes, and complete pieces with notes on what they learned along the way.

    This is all about process, not perfection—of crucial importance to creativity.

    3. Real-World, Applied Assessments

    Inject reality into assessment.

    • Science: Instead of memorizing the water cycle, students develop a community plan to reduce waste of water.
    • Business: Instead of solving abstract formulas in school, students pitch a mini start-up idea, budget, marketing, and ethical limitations.

    These exercises reveal whether students can translate theory into practice.

    4. Socratic Seminars & Oral Defenses

    When students explain their thought process verbally and respond to questions, it reflects depth of understanding.

    • Example: Following in a research paper, the student has 10 minutes of Q&A with peers or teacher.

    If they can hold their ground in defending their argument, adapt when challenged, and expound under fire, it is a sign of actual mastery.

    5  Reflection & Metacognition

    Asking students to reflect on their own learning makes them more self-aware thinkers.

    Example questions:

    • “What area of this project challenged you most, and how did you cope?”
    • “If you were to begin again, what would you do differently?”

    This is not right or wrong—it’s developing self-knowledge, a critical lock to lifelong learning.

    6. Collaborative & Peer Assessment

    Learning is a social process. Permitting students to evaluate or draw on each other’s work reveals how they think in dialogue.

    • Example: In a group project, each student writes a short memo on their piece and how they wove others’ ideas together.

    Collaboration skills are harder to fake, but critically necessary for work and civic life.

    The Human Side

    Assessing deeper learning is more time-consuming, labor-intensive, and occasionally subjective. It’s not just a matter of grading a multiple-choice test. But it also respects students as human beings, rather than test-takers.

    It tells students:

    • We value your thoughts, not just your recall.
    • Mistakes and revisions are part of the process of getting better.
    • Your own opinion matters.

    This makes testing less of a trap and more of an honest reflection of real learning.

     Last Reflection

    While recall tests shout, “What do you know?”, deeper tests whisper, “What can you do with what you know?” That’s all the difference in an AI age. Machines can recall facts instantly—but only humans can balance ethics, see futures, design relationships, and make sense.

    The future of assessment has to be less about efficiency and more about authenticity. Because what’s on the line is not grades—it’s preparing students for a chaotic, uncertain world.

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Anonymous
Asked: 14/08/2025In: Communication, News, Technology

How are global supply chains adapting to new tariff policies?

new tariff policies

aitechnology
  1. Anonymous
    Anonymous
    Added an answer on 14/08/2025 at 4:15 pm

    International supply chains are adapting to be more agile than ever to the latest tariff regimes — pretty much like an old traveler forced to shift flight paths halfway through the journey. This is what's going down on the ground: Rebasing trade routes – Businesses are redirecting sourcing from natiRead more

    International supply chains are adapting to be more agile than ever to the latest tariff regimes — pretty much like an old traveler forced to shift flight paths halfway through the journey.

    This is what’s going down on the ground:

    Rebasing trade routes – Businesses are redirecting sourcing from nations impacted with increased tariffs to nations with more amicable terms of trade. For instance, a company that previously depended on China would now diversify vendors in Vietnam, Mexico, or Eastern Europe.

    “Friendshoring” and regional hubs – Rather than a single massive manufacturing hub, supply chains are fragmenting into regional webs to manage risk. In this manner, if one trade lane becomes pricey or clogged, the others continue going.

    Tech-powered forecasting – AI and analytics are enabling firms to model “what if” tariff situations so they can reconfigure orders, shipping routes, and pricing before issues arise.

    Revival of local production – Increased tariffs make imports more expensive, so some businesses are taking some production steps in-house — creating local employment but also redefining cost profiles.

    Why it feels so human:

    Companies aren’t merely juggling figures; they’re being flexible and ingenious. Just as individuals learn to live with unexpected shifts in their own household budgets, companies are getting better at making shrewder trade-offs — safeguarding what’s most important while leveraging innovation to stay alive.

    Briefly put, tariffs are making supply chains more like nimble gymnasts than rigid production lines — agile, diversified, and able to roll with the punches.

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

How can generative-AI tools be integrated into teaching so that they augment rather than replace educators?

generative-AI tools be integrated int ...

ai in educationeducational technologygenerative ai toolsresponsible ai useteacher augmentationteaching enhancement
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice
    Added an answer on 25/11/2025 at 3:49 pm

    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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mohdanasMost Helpful
Asked: 24/09/2025In: Technology

How do multimodal AI systems (text, image, video, voice) change the way we interact with machines compared to single-mode AI?

text, image, video, voice change the ...

computervisionfutureofaihumancomputerinteractionmachinelearningmultimodalainaturallanguageprocessing
  1. mohdanas
    mohdanas Most Helpful
    Added an answer on 24/09/2025 at 10:37 am

    From Single-Mode to Multimodal: A Giant Leap All these years, our interactions with AI have been generally single-mode. You wrote text, the AI came back with text. That was single-mode. Handy, but a bit like talking with someone who could only answer in written notes. And then, behold, multimodal AIRead more

    From Single-Mode to Multimodal: A Giant Leap

    All these years, our interactions with AI have been generally single-mode. You wrote text, the AI came back with text. That was single-mode. Handy, but a bit like talking with someone who could only answer in written notes.

    And then, behold, multimodal AI — computers capable of understanding and producing in text, image, sound, and even video. Suddenly, the dialogue no longer seems so robo-like but more like talking to a colleague who can “see,” “hear,” and “talk” in different modes of communication.

    Daily Life Example: From Stilted to Natural

    Ask a single-mode AI: “What’s wrong with my bike chain?”

    • With text-only AI, you’d be forced to describe the chain in its entirety — rusty, loose, maybe broken. It’s awkward.
    • With multimodal AI, you just take a picture, upload it, and the AI not only identifies the issue but maybe even shows a short video of how to fix it.

    It’s staggering: one is like playing guessing game, the other like having a friend with you.

    Breaking Down the Changes in Interaction

    • From Explaining to Showing

    Instead of describing a problem in words, we can show it. That brings the barrier down for language, typing, or technology-phobic individuals.

    • From Text to Simulation

    A text recipe is useful, but an auditory, step-by-step video recipe with voice instruction comes close to having a cooking coach. Multimodal AI makes learning more interesting.

    • From Tutorials to Conversationalists

    With voice and video, you don’t just “command” an AI — you can have a fluid, back-and-forth conversation. It’s less transactional, more cooperative.

    • From Universal to Personalized

    A multimodal system can hear you out (are you upset?), see your gestures, or the pictures you post. That leaves room for empathy, or at least the feeling of being “seen.”

    Accessibility: A Human Touch

    • One of the most powerful is the way that this shift makes AI more accessible.
    • A blind person can listen to image description.
    • A dyslexic person can speak their request instead of typing.
    • A non-native speaker can show a product or symbol instead of wrestling with word choice.
    • It knocks down walls that text-only AI all too often left standing.

    The Double-Edged Sword

    Of course, it is not without its problems. With image, voice, and video-processing AI, privacy concerns skyrocket. Do we want to have devices interpret the look on our face or the tone of anxiety in our voice? The more engaged the interaction, the more vulnerable the data.

    The Humanized Takeaway

    Multimodal AI makes the engagement more of a relationship than a transaction. Instead of telling a machine to “bring back an answer,” we start working with something which can speak in our native modes — talk, display, listen, show.

    It’s the contrast between reading a directions manual and sitting alongside a seasoned teacher who teaches you one step at a time. Machines no longer feel like impersonal machines and start to feel like friends who understand us in fuller, more human ways.

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daniyasiddiquiEditor’s Choice
Asked: 26/11/2025In: Digital health, Health

How can we ensure interoperability and seamless data-integration across health systems?

we ensure interoperability and seamle ...

data integrationelectronic health records (ehr)health informaticshealth itinteroperability
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice
    Added an answer on 26/11/2025 at 2:29 pm

    1. Begin with a common vision of “one patient, one record.” Interoperability begins with alignment, not with software. Different stakeholders like hospitals, insurers, public health departments, state schemes, and technology vendors have to agree on one single principle: Every patient is entitled toRead more

    1. Begin with a common vision of “one patient, one record.”

    Interoperability begins with alignment, not with software.

    Different stakeholders like hospitals, insurers, public health departments, state schemes, and technology vendors have to agree on one single principle:

    Every patient is entitled to a unified, longitudinal, lifetime health record, available securely whenever required.

    Without this shared vision:

    • Systems compete instead of collaborate.
    • Vendors build closed ecosystems
    • instead, data is treated as an “asset” by hospitals, rather than as a public good.
    • public health programs struggle to see the full population picture.

    A patient should not carry duplicate files, repeat diagnostics, or explain their medical history again and again simply because systems cannot talk to each other.

    2. Adopt standards, not custom formats: HL7 FHIR, SNOMED CT, ICD, LOINC, DICOM.

    When everyone agrees on the same vocabulary and structure, interoperability then becomes possible.

    This means:

    • FHIR for data exchange
    • SNOMED CT for clinical terminology
    • ICD-10/11 for diseases
    • LOINC for laboratory tests
    • DICOM for imaging

    Data flows naturally when everyone speaks the same language.

    A blood test from a rural PHC should look identical – digitally – to one from a corporate hospital; only then can information from dashboards, analytics engines, and EHRs be combined without manual cleaning.

    This reduces clinical errors, improves analytics quality, and lowers the burden on IT teams.

    3. Build APIs-first systems, not locked databases.

    Modern health systems need to be designed with APIs as the backbone, not after the fact.

    APIs enable:

    • real-time data sharing
    • Connectivities between public and private providers.
    • Integration with telemedicine apps, wearables, diagnostics
    • automated validation and error report generation

    An APIs-first architecture converts a health system from a silo into an ecosystem.

    But critically, these APIs must be:

    • secure
    • documented
    • version-controlled
    • validated
    • governed by transparent rules

    Otherwise, interoperability becomes risky, instead of empowering.

    4. Strengthen data governance, consent, and privacy frameworks.

    Without trust, there is no interoperability.

    And there will not be trust unless the patients and providers feel protected.

    To this end:

    • Patients should be in control of their data, and all consent flows should be clear.
    • access must be role based and auditable
    • Data minimization should be the rule, not the exception.
    • Sharing of data should be guided by standard operating procedures.
    • independent audits should verify compliance

    If people feel that their data will be misused, they will resist digital health adoption.

    What is needed is humanized policymaking: the patient must be treated with respect, not exposed.

    5. Gradual, not forced migration of legacy systems.

    Many public hospitals and programs still rely on legacy HMIS, paper-based processes, or outdated software.

    Trying to forcibly fit old systems into modern frameworks overnight, interoperability fails.

    A pragmatic, human-centered approach is:

    • Identify high-value modules for upgrade, such as registration, lab, and pharmacy.
    • Introduce middleware that will convert legacy formats to new standards.
    • Train the personnel before process changeovers.
    • Minimize disruption to clinical workflows.

    Digital transformation only succeeds when clinicians and health workers feel supported and not overwhelmed.

    6. Invest in change management and workforce capacity-building.

    Health systems are, after all, run by people: doctors, nurses, health facility managers, data entry operators, and administrators.

    Even the most advanced interoperability framework will fail if:

    • personnel are not trained
    • workflows are not redesigned
    • clinicians resist change.
    • Data entry remains inconsistent.
    • incentive systems reward old processes

    Interoperability becomes real when people understand why data needs to flow and how it improves care.

    Humanized interventions:

    • hands-on training
    • simple user interfaces
    • clear SOPs
    • local language support
    • Digital Literacy Programs
    • Continuous helpdesk and support systems

    The human factor is the hinge on which interoperability swings.

    7. Establish health data platforms that are centralized, federated, or hybrid.

    Countries and states must choose models that suit their scale and complexity:

    Centralized model

    All information is maintained within one large, single national or state-based database.

    • easier for analytics, dashboards, and population health
    • Stronger consistency
    • But more risk if the system fails or is breached

    Federated model

    Data remains with the data originators; only metadata or results are shared

    • Stronger privacy
    • easier for large federated governance structures-e.g., Indian states
    • requires strong standards and APIs

    Hybrid model (most common)

    • It combines centralized master registries with decentralized facility systems.
    • enables both autonomy and integration

    The key to long-term sustainability is choosing the right architecture.

    8. Establish HIEs that organize the exchange of information.

    HIEs are the “highways” for health data exchange.

    They:

    • validate data quality
    • consent management
    • authenticate users
    • handle routing and deduplication
    • ensure standards are met

    This avoids point-to-point integrations, which are expensive and fragile.

    The India’s ABDM, UK’s NHS Spine, and US HIE work on this principle.

    Humanized impact: clinicians can access what they need without navigating multiple systems.

    9. Assure vendor neutrality and prevent monopolies.

    When interoperability dies:

    • vendors lock clients into proprietary formats
    • migrating systems is not easy for hospitals.
    • licensing costs become barriers
    • commercial interests are placed above standards.

    Procurement policies should clearly stipulate:

    • FHIR compliance
    • open standards
    • data portability
    • source code escrow for critical systems

    A balanced ecosystem enables innovation and discourages exploitation.

    10. Use continuous monitoring, audit trails and data quality frameworks.

    Interoperability is not a “set-and-forget” achievement.

    Data should be:

    • validated for accuracy
    • checked for completeness
    • monitored for latency
    • audited for misuse
    • Governed by metrics, such as HL7 message success rate, FHIR API uptime

    Data quality translates directly to clinical quality.

    Conclusion Interoperability is a human undertaking before it is a technical one.

    In a nutshell

    seamless data integration across health systems requires bringing together:

    • shared vision
    • global standards
    • API-based architectures
    • strong governance
    • change management
    • training
    • open ecosystems
    • vendor neutrality

    Continuous Monitoring In the end, interoperability succeeds when it enhances the human experience:

    • A mother with no need to carry medical files.
    • A doctor who views the patient’s entire history in real time.
    • A public health team able to address early alerts of outbreaks.
    • An insurer who processes claims quickly and settles them fairly.
    • A policymaker who sees real-time population health insights.

    Interoperability is more than just a technology upgrade.

    It is a foundational investment in safer, more equitable, and more efficient health systems.

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mohdanasMost Helpful
Asked: 06/09/2025In: Health, News

Is the rise of ultra-processed foods the biggest health crisis of our time?

ultra-processed foods the biggest hea ...

health
  1. mohdanas
    mohdanas Most Helpful
    Added an answer on 06/09/2025 at 12:42 pm

    A Secret Crisis on Our Plates When individuals say "ultra-processed foods," they're describing foods that have been highly processed from their natural state—bagged snacks, instant noodles, sweet drinks, frozen ready-to-eat meals, or even certain breakfast cereals. These foods tend to be created toRead more

    A Secret Crisis on Our Plates

    When individuals say “ultra-processed foods,” they’re describing foods that have been highly processed from their natural state—bagged snacks, instant noodles, sweet drinks, frozen ready-to-eat meals, or even certain breakfast cereals. These foods tend to be created to be super-tasty, convenient, and affordable. On the surface, it sounds like advancement—less time spent cooking, more shelf time, and tastes everyone seems to enjoy. But beneath the convenience comes a steep health price.

    Why Ultra-Processed Foods Matter

    The issue isn’t merely that they’re “junk” in a classical sense. They’re engineered to rewire the way our brains and bodies react to food. They contain lots of sugar, salt, unhealthy fats, and additives that tend to deceive our natural satiety signals, and it’s easy to overconsume. This over time adds up to accelerating obesity, type 2 diabetes, heart disease, and even some cancers. Meanwhile, other nutrients get sacrificed on the altar of convenience, flavor, and affordability.

    In most countries, ultra-processed foods constitute over half of the total calories consumed every day by the average individual. Whole foods like fruits, vegetables, grains, legumes, and minimally processed staples get edged out of the diet because of it. It is no longer a matter of personal choice; it’s a matter of the food environment that we have.

    A Global Health Concern

    What makes this issue particularly alarming is how global it’s become. In wealthier nations, ultra-processed foods dominate grocery store shelves, while in developing countries, they’re aggressively marketed as symbols of modern living. Walk through a supermarket in any city, and you’ll see bright packaging and low prices that make these foods nearly irresistible.

    The payoff? Increased rates of lifestyle disease at all economic levels. That is especially troubling for children. Much of the way kids are developing taste buds is used to favor the sweetness of soda over water or chips over raw vegetables. That forms habits that last a lifetime.

    Beyond Physical Health

    There is also a mental health component. New evidence associates consumption of ultra-processed foods with increased depression and anxiety rates. Although the science is in its early stages, it questions what impact the foods we consume have on not only our bodies but also on our minds.

    Is It the Biggest Health Crisis?

    Labeling it the biggest health crisis is no hyperbole. Yes, infectious diseases, pandemics, and global health risks linked to climate still loom large. But in contrast with those, the crisis of ultra-processed foods is creeping, usually unnoticed from day to day, and thoroughly entrenched in our habits. It’s more difficult to mobilize against because it does not present itself as a direct danger—until it manifests in the form of increased healthcare expenditures, diminished life expectancy, and generations of individuals living with treatable chronic diseases.

    Finding a Way Forward

    The encouraging news is that people are becoming more aware. Governments are coming out with warning labels, sugar taxes, and limits on marketing to kids. Neighborhoods are demanding availability of fresh, local produce. And individually, individuals are rediscovering the importance of preparing simple meals, even on a small scale.

    The challenge, however, isn’t simply one of individual willpower. It’s about restructuring food systems so that healthier options are the easier, cheaper ones. Because right now, convenience tends to prevail—and ultra-processed foods are prevailing on that front.

    In several respects, the increase in ultra-processed foods is one of the biggest health emergencies of our era—not because individuals are “making bad choices,” but because the infrastructure around us has been designed to lead us to make unhealthy choices by default. Addressing it will involve more than individual willpower; it will involve cultural transformation, policy adjustments, and reimagining what we envision the future of food to be.

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mohdanasMost Helpful
Asked: 22/11/2025In: Education

How is generative AI (e.g., large language models) changing the roles of teachers and students in higher education?

the roles of teachers and students in ...

aiineducationedtechgenerativeaihighereducationllmteachingandlearning
  1. mohdanas
    mohdanas Most Helpful
    Added an answer on 22/11/2025 at 2:10 pm

    1. The Teacher's Role Is Shifting From "Knowledge Giver" to "Knowledge Guide" For centuries, the model was: Teacher = source of knowledge Student = one who receives knowledge But LLMs now give instant access to explanations, examples, references, practice questions, summaries, and even simulated tutRead more

    1. The Teacher’s Role Is Shifting From “Knowledge Giver” to “Knowledge Guide”

    For centuries, the model was:

    • Teacher = source of knowledge
    • Student = one who receives knowledge

    But LLMs now give instant access to explanations, examples, references, practice questions, summaries, and even simulated tutoring.

    So students no longer look to teachers only for “answers”; they look for context, quality, and judgment.

    Teachers are becoming:

    Curators-helping students sift through the good information from shallow AI responses.

    • Critical thinking coaches: teaching students to question the output of AI.
    • Ethical mentors: to guide students on what responsible use of AI looks like.
    • Learning designers: create activities where the use of AI enhances rather than replaces learning.

    Today, a teacher is less of a “walking textbook” and more of a learning architect.

     2. Students Are Moving From “Passive Learners” to “Active Designers of Their Own Learning”

    Generative AI gives students:

    • personalized explanations
    • 24×7 tutoring
    • project ideas
    • practice questions
    • code samples
    • instant feedback

    This means that learning can be self-paced, self-directed, and curiosity-driven.

    The students who used to wait for office hours now ask ChatGPT:

    • “Explain this concept with a simple analogy.
    • “Help me break down this research paper.”
    • “Give me practice questions at both a beginner and advanced level.”
    • LLMs have become “always-on study partners.”

    But this also means that students must learn:

    • How to determine AI accuracy
    • how to avoid plagiarism
    • How to use AI to support, not replace, thinking
    • how to construct original arguments beyond the generic answers of AI

    The role of the student has evolved from knowledge consumer to co-creator.

    3. Assessment Models Are Being Forced to Evolve

    Generative AI can now:

    • write essays
    • solve complex math/engineering problems
    • generate code
    • create research outlines
    • summarize dense literature

    This breaks traditional assessment models.

    Universities are shifting toward:

    • viva-voce and oral defense
    • in-class problem-solving
    • design-based assignments
    • Case studies with personal reflections
    • AI-assisted, not AI-replaced submissions
    • project logs (demonstrating the thought process)

    Instead of asking “Did the student produce a correct answer?”, educators now ask:

    “Did the student produce this? If AI was used, did they understand what they submitted?”

    4. Teachers are using AI as a productivity tool.

    Teachers themselves are benefiting from AI in ways that help them reclaim time:

    • AI helps educators
    • draft lectures
    • create quizzes
    • generate rubrics
    • summarize student performance
    • personalize feedback
    • design differentiated learning paths
    • prepare research abstracts

    This doesn’t lessen the value of the teacher; it enhances it.

    They can then use this free time to focus on more important aspects, such as:

    • deeper mentoring
    • research
    • Meaningful 1-on-1 interactions
    • creating high-value learning experiences

    AI is giving educators something priceless in time.

    5. The relationship between teachers and students is becoming more collaborative.

    • Earlier:
    • teachers told students what to learn
    • students tried to meet expectations

    Now:

    • both investigate knowledge together
    • teachers evaluate how students use AI.
    • Students come with AI-generated drafts and ask for guidance.
    • classroom discussions often center around verifying or enhancing AI responses
    • It feels more like a studio, less like a lecture hall.

    The power dynamic is changing from:

    • “I know everything.” → “Let’s reason together.”

    This brings forth more genuine, human interactions.

    6. New Ethical Responsibilities Are Emerging

    Generative AI brings risks:

    • plagiarism
    • misinformation
    • over-reliance
    • “empty learning”
    • biased responses

    Teachers nowadays take on the following roles:

    • ethics educators
    • digital literacy trainers
    • data privacy advisors

    Students must learn:

    • responsible citation
    • academic integrity
    • creative originality
    • bias detection

    AI literacy is becoming as important as computer literacy was in the early 2000s.

    7. Higher Education Itself Is Redefining Its Purpose

    The biggest question facing universities now:

    If AI can provide answers for everything, what is the value in higher education?

    The answer emerging from across the world is:

    • Education is not about information; it’s about transformation.

    The emphasis of universities is now on:

    • critical thinking
    • Human judgment
    • emotional intelligence
    • applied skills
    • teamwork
    • creativity
    • problem-solving
    • real-world projects

    Knowledge is no longer the endpoint; it’s the raw material.

     Final Thoughts A Human Perspective

    Generative AI is not replacing teachers or students, it’s reshaping who they are.

    Teachers become:

    • guides
    • mentors
    • facilitators
    • ethical leaders
    • designers of learning experiences

    Students become:

    • active learners
    • critical thinkers

    co-creators problem-solvers evaluators of information The human roles in education are becoming more important, not less. AI provides the content. Human beings provide the meaning.

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