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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daniyasiddiquiEditor’s Choice
Asked: 23/12/2025In:

few-shot, one-shot, and zero-shot pro ...

aiconceptschatgptfewshotllmsoneshotzeroshot
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. Zero Shot Prompting: “Just Do It In zero-shot prompting, the AI will be provided with only the instruction and without any example at all. It is expected that the model will be completely dependent on its previous training knowledge. What it looks like: Simply tell the AI what you want. Example:Read more

    1. Zero Shot Prompting: “Just Do It

    In zero-shot prompting, the AI will be provided with only the instruction and without any example at all. It is expected that the model will be completely dependent on its previous training knowledge.

    What it looks like:

    • Simply tell the AI what you want.

    Example:

    • “Classify the email below as spam or not spam.”
    • There are no examples given. The computer uses what it already knows about spam patterns to make decisions.

    When zero-shot learning is most helpful:

    • “The task is simple or common” is one example of
    • The instruction is clear and unequivocal
    • You expect quick answers with small inputs.
    • Costs and latency are considerations
    • Limitations
    • Results can vary depending on the nature of the activity, especially when it is
    • Less reliable for domain-specific or complex tasks
    • “AI can interpret a task differently than its human author intended”

    In other words, zero-shot is like saying, “That’s the job, now go,” to a new employee.

    “2. One-Shot Prompting: “Here’s

    In one-shot prompting, you provide an example of what you would like the AI to produce. This example example helps to align the AI’s understanding of what you are trying to get across.

    What it looks like:

    step 1.

    you give one example. Then comes the actual question.

    • # Example
    • “Example
    • Email: You have won a free prize!
      → Spam

    This can be considered as:

    • “Your meeting is scheduled for tomorrow.”
    • This example alone helps to explain the structure and reasoning required.

    One-shot is good when:

    • There is more than one way of interpreting this task
    • You want to control format or tone
    • “The zero-shot results were inconsistent”
    • You want greater accuracy without a lengthy prompt

    Limitations

    • One Example May Still Not Include Edge Cases
    • Marginally higher usage than zero shot

    Step 2.

    • Whether quality is important or not also depends on how good an example is
      While quality is
    • One shot prompting is like: “Here’s one sample, do it like this.” Examples are: 1. When

    3. Few-Shot Prompting: “Learn from These

    Few-shot prompting involves several examples prior to the task at hand. Examples aid the AI in pattern recognition to enable pattern application.

    What it looks like:

    • There are various pairs of input and output that you provide, followed by asking the model to continue.

    Example:

    Example 1:

    • Review: ‘Excellent product!’ → Positive

    Example 2:

    • Explanation: ‘Very disappointing experience.’ → Negative

    Now classify:

    • “The service was okay, not great.”
    • The AI infers sentiment patterns based on the examples.

    When few-shot is best:

    • The problem is complex or domain-specific
    • There has to be strict precision in the output format being followed
    • You require more reliability and consistencies
    • You want the machine to trace a specific path of reasoning

    Limitations

    • Longer prompts are associated with higher costs as well as higher latency
    • There are too many examples to list them all out
    • Not scalable in the case of large or dynamic knowledge bases

    Few-shot prompting is analogous to teaching a person several example solutions before assigning them an exercise.

    How This Is Used in Real Systems

    In real-world AI applications:

    Zero-shot is common for chatbots on general questions

    One-shot: When formatting or tone issues are involved few shot is employed in business operations, assessments, and output. Frequently, the team begins with zero-shot learning and increases the data gradually until the outcomes are satisfactory.

    Key Takeaways

    Zero-shot example: “Do this task
    One-shot: “Here’s one example, do it like this.
    Few-shot: “Here are multiple examples follow the pattern.”

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daniyasiddiquiEditor’s Choice
Asked: 08/10/2025In:

the global economic recovery in 2026

economic recoveryglobal tradeinflationsupply chain disruptionstariffstrade policy
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    How tariffs slow an economy (the simple mechanics) Higher import prices → weaker demand. Tariffs raise the cost of imported inputs and final goods. Companies either pay more for raw materials and intermediate goods (squeezing margins) or pass costs to consumers (reducing purchasing power). That combRead more

    How tariffs slow an economy (the simple mechanics)

    • Higher import prices → weaker demand. Tariffs raise the cost of imported inputs and final goods. Companies either pay more for raw materials and intermediate goods (squeezing margins) or pass costs to consumers (reducing purchasing power). That combination cools consumption and industrial activity.
    • Supply-chain disruption & re-shoring costs. Firms respond by reconfiguring supply chains (finding new suppliers, on-shoring, or stockpiling). Those adjustments are expensive and slow to pay off — in the near term they reduce investment and efficiency.
    • Investment chill from uncertainty. The prospect of escalating or unpredictable tariffs raises policy uncertainty. Businesses delay or scale back capital projects until trade policy stabilizes.
    • Retaliation and cascading barriers. Tariffs often trigger retaliatory measures. When many countries raise barriers, global trade volumes fall, which hits export-dependent economies and global value chains.

    These channels are exactly why multilateral agencies and market analysts say tariffs and trade restrictions can lower growth even when headline GDP still looks “resilient.”

    What the major institutions say (quick reality check)

    • The IMF’s recent updates show modest global growth in 2025–26 but flag tariff-driven uncertainty as a downside risk. Their 2025 WEO update projects global growth near 3.0% for 2025 and 3.1% for 2026 while explicitly warning that higher tariffs and policy uncertainty are important risks.
    • The OECD and several analysts argue the full force of recent tariff shocks hasn’t been felt yet — and they project growth weakening in 2026 as front-loading of imports ahead of tariffs wears off and higher effective tariff rates bite. The OECD’s interim outlook expects a slowdown in 2026 tied to these effects.
    • The WTO and World Bank also report trade-volume weakness and flag trade barriers as a material drag on trade growth — which feeds into lower global GDP.
    • These institutions are not predicting a single global recession just from tariffs, but they do expect measurable downward pressure on trade and investment, which slows recovery momentum.

    How big could the hit be? (it depends — but here are the drivers)

    Magnitude depends on policy breadth and persistence. Small, narrow tariffs on a few goods will only nudge growth; widespread, high tariffs across major economies (or sustained tit-for-tat escalation) can shave sizable tenths of a percentage point off global growth. Analysts point out that front-loading (firms buying ahead of tariff implementation) can temporarily buoy trade, but once that fades the negative effects appear.

    Timing matters. If tariffs are announced and then held in place for years, businesses will invest in duplicative capacity and the re-allocation costs accumulate. That’s the scenario most likely to slow growth into 2026.
    Bloomberg

    Who loses most

    • Export-dependent emerging markets (small open economies and commodity exporters) suffer when demand falls in advanced markets or when their inputs become more expensive.
    • Complex-value-chain industries (autos, electronics, semiconductors) where components cross borders many times are particularly vulnerable to tariffs and retaliations.
    • Low-income countries feel second-round effects: slower global growth → weaker commodity prices → less fiscal space and elevated debt stress. The World Bank notes growth downgrades when trade restrictions rise.
      World Bank

    Knock-on effects for inflation and policy

    Tariffs can be inflationary (higher import prices), which puts central banks in a bind: tighten to fight inflation and risk choking off growth, or tolerate higher inflation and risk de-anchored expectations. Either choice complicates recovery and could reduce real incomes and investment. Several policymakers have voiced concern that the mix of tariffs plus high policy uncertainty creates a stagflation-like risk in vulnerable economies.

    Offsets and reasons the slowdown may be limited

    • Front-loading and substitution. Businesses sometimes build inventories or substitute suppliers — that mutes immediate trade declines. IMF and other agencies note that some front-loading actually supported 2024–2025 trade figures, but this effect runs out.
    • Fiscal and monetary support. Governments can cushion the blow with targeted fiscal spending, subsidies, or trade facilitation. But those measures have limits (fiscal space, political will) and can’t fully replace cross-border trade flows.
    • Near-term resilience in consumption. Private sectors in some major economies have remained resilient, which helps growth hold up even as trade cools. But resilience erodes if tariffs persist and investment dries up.
      Reuters

    Practical indicators to watch in 2025–26 (what will tell us the story)

    • Trade volumes (WTO merchandise trade stats): a sustained drop signals broad tariff damage.
    • Business investment and capex plans: continued delays or cancellations point to a deeper investment chill.
    • Manufacturing PMI and global supply-chain bottlenecks: weakening PMIs across manufacturing hubs show cascading effects.
    • Inflation vs. growth trade-offs and central bank minutes: whether monetary policy tightens in response to tariff-driven inflation.
    • Announcements of trade retaliation or new tariff rounds: escalation increases downside risk; diplomatic rollbacks reduce it.

    Bottom line — a human takeaway

    Tariffs won’t necessarily cause an immediate, synchronized global recession in 2026, but they are a clear and credible downside risk to the fragile recovery. They act like a slow-moving tax on trade: higher costs, muddled investment decisions, and weaker demand — combined effects that shave growth and worsen inequalities between export-dependent and more closed economies. Policymakers can limit the damage with diplomacy, targeted support for affected industries and countries, and clear timelines — but if protectionism persists or escalates, the global recovery will be noticeably weaker in 2026 than it might otherwise have been.

    If you want, I can:

    • Turn this into a one-page slide for a briefing (executive summary + 3 charts of trade volume, investment plans, and projected growth scenarios); or
    • Pull the most recent WTO/OECD/IMF bullets (with dates and one-sentence takeaways) to cite in a short memo.

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daniyasiddiquiEditor’s Choice
Asked: 09/08/2025In: ,

ai
  1. Anonymous
    Anonymous

    Seeing, Hearing, and Comprehending — Simultaneously Multimodal AI models are akin to human beings who can see, hear, and read simultaneously — but with the speed of a supercomputer. Rather than processing single inputs (such as text), these models blend vision, speech, and text to make more intelligRead more

    Seeing, Hearing, and Comprehending — Simultaneously
    Multimodal AI models are akin to human beings who can see, hear, and read simultaneously — but with the speed of a supercomputer. Rather than processing single inputs (such as text), these models blend vision, speech, and text to make more intelligent, faster decisions in real-time.

    How They Do It

    • Vision

    The AI can “see” through videos, images, or live camera streams — identifying objects, recognizing text in images, or examining environments.

    • Speech

    It can “hear” and interpret spoken words, tone, or background sounds.

    • Text

    It can analyze written commands, documents, or live chat input in real time.

    By merging these streams, the AI constructs a comprehensive image of what’s happening before deciding on the next course of action.

    Real-World Examples

    • Healthcare

    A hospital AI might monitor a patient’s vital signs on a screen (vision), hear their breathing (speech), and read the doctor’s notes (text) — and alert physicians in real-time if anything’s amiss.

    • Autonomous Vehicles

    Check, safe driving decisions. A driverless vehicle can see people walking, hear sirens, and read signs at the same time to make qui

    • Customer Support

    A service bot can observe a customer’s video stream, hear their tone of voice, and see the chat text to deliver the most empathetic reply.

    Why It Matters

    This combination makes AI more context-aware, decreasing misunderstandings and enhancing safety in high-stakes environments. It’s not being clever — it’s being situationally clever, such as a human being able to read the room.

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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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daniyasiddiquiEditor’s Choice
Asked: 12/10/2025In:

India upgrading its engagement with t ...

diplomatic recognitionembassy reopeningforeign policyindia–afghanistan relationss. jaishankartaliban government
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    India’s Renewed Outreach to Afghanistan: A Delicate Diplomatic Shift Yes, India is indeed upgrading its engagement with the Taliban government in Afghanistan and is reportedly planning to reopen its embassy in Kabul after more than three years of limited operations. This marks a significant — and caRead more

    India’s Renewed Outreach to Afghanistan: A Delicate Diplomatic Shift

    Yes, India is indeed upgrading its engagement with the Taliban government in Afghanistan and is reportedly planning to reopen its embassy in Kabul after more than three years of limited operations. This marks a significant — and cautious — recalibration in New Delhi’s foreign policy toward a country with which it shares deep historical, cultural, and economic ties.

    Background: From Withdrawal to

    Reconnection

    When the Taliban seized power in August 2021, India, like most other nations, swiftly evacuated its diplomats and suspended its official presence in Kabul. At that time, New Delhi’s stance was one of wait and watch, reflecting deep concern about the Taliban’s past links to terrorism and their implications for India’s security interests, particularly regarding Pakistan-based extremist groups.

    But ever since the past two years, ground realities have shifted. The Taliban, as it sought world legitimacy and economic relief, was more amenable to initiate negotiations. India, for its part, realizes that it is neither strategically nor long-term viable to fully isolate Afghanistan — especially since China, Pakistan, Iran, and Russia have all maintained or expanded their presence in Afghanistan.

     Plans to Reopen the Embassy

    It is said that India has been making logistical and security preparations to re-establish its full-fledged embassy in Kabul, which has been operating in a limited form since 2022 under a “technical mission.”

    It has largely handled the distribution of humanitarian assistance, monitoring of development projects, and visas for Afghan students and patients traveling to India.

    A formal re-opening would be India’s most openly diplomatic engagement with the Taliban government so far — an exercise of pragmatism and symbolism. It signifies India’s desire to exercise influence over Afghanistan and protect its investments, which amount to over $3 billion in infrastructure and relief activities since 2001.

     India’s Strategic Motivations

    India’s fresh initiative is driven by a mix of security, economic, and geopolitical interests:

    • Counteracting Pakistani Influence: Pakistan has dominated Kabul for decades. Reopening an embassy enables India to restore a foothold and ensure that Afghan ground is not used against India.
    • Humanitarian Obligation: India has supplied wheat, medicine, and COVID-19 shots to Afghanistan despite the Taliban regime. Strengthening diplomatic ties enables smoother delivery of aid to Afghans.
    • Regional Stability: A stable Afghanistan is beneficial to India’s connectivity and trade interests in Central Asia, particularly under projects like the Chabahar Port and the International North-South Transport Corridor (INSTC).
    • Engagement over Isolation: India prefers to engage the de facto powers to influence developments rather than letting a vacuum fall into the lap of their rivals like China or Pakistan.

    Diplomatic Tightrope: Recognition vs. Engagement

    It must be noted that India has not yet recognized the Taliban regime officially, but nor will it do so at this time. It’s an issue of practical engagement more than political approval in order to restore its embassy.

    • New Delhi continues to hold out for inclusive politics, women’s empowerment, and counter-terror commitments as the terms of full diplomatic recognition.

    This realistic approach allows India to defend its interests without deviating from the general international belief of action under the leadership of the United Nations.

    Broader Implications & International Reactions

    • The international community has largely interpreted India’s action as a pragmatic and necessary step. The Western nations, many of whom have limited contact with the Taliban, view India as a trusted interlocutor who can help moderate the regime’s attitude.
    • While Afghans themselves, above all those recipients of Indian scholarships, medical aid, and development initiatives — have in general been welcoming the shift as one made by a friend over a long time, rather than an exchange ally.
    • India’s re-engagement with Afghanistan during the Taliban period is a diplomatic balance of the tightrope kind — a balancing act that is a mix of realism and humanitarian sensitivities. By reopening its embassy and upgrading relations, New Delhi aims to be a player in the changing political landscape of Afghanistan, protect its people-to-people ties, and prevent the country slipping further into isolation.

    It is a modest but important shift — one that reflects India’s growing self-assurance as a regional power that can promote its national interests without compromising moral and strategic imperatives.

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daniyasiddiquiEditor’s Choice
Asked: 28/12/2025In:

prompt engineering different from tra ...

artificialintelligencegenerativeailargelanguagemodelsmachinelearningmodeltraining
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    What Is Traditional Model Training Conventional training of models is essentially the development and optimization of an AI system by exposing it to data and optimizing its internal parameters accordingly. Here, the team of developers gathers data from various sources and labels it and then employsRead more

    What Is Traditional Model Training

    Conventional training of models is essentially the development and optimization of an AI system by exposing it to data and optimizing its internal parameters accordingly. Here, the team of developers gathers data from various sources and labels it and then employs algorithms that reduce an error by iterating numerous times.

    While training, the system will learn about the patterns from the data over a period of time. For instance, an email spam filter system will learn to categorize those emails by training thousands to millions of emails. If the system is performing poorly, engineers would require retraining the system using better data and/or algorithms.

    This process usually involves:

    • Huge amounts of quality data
    • High computing power (GPUs/TP
    • Time-consuming experimentation and validation
    • Machine learning knowledge for specialized applications

    After it is trained, it acts in a way that cannot be changed much until it is retrained again.

    What is Prompt Engineering?

    “Prompt Engineering” is basically designing and fine-tuning these input instructions or prompts to provide to a pre-trained model of AI technology, and specifically large language models to this point in our discussion, so as to produce better and more meaningful results from these models. The technique of prompt engineering operates at a purely interaction level and does not necessarily adjust weights.

    In general, the prompt may contain instructions, context, examples, constraints, and/or formatting aids. As an example, the difference between the question “summarize this text” and “summarize this text in simple language for a nonspecialist” influences the response to the question asked.

    Prompt engineering is based on:

    • Clear and well-structured instructions
    • Establishing Background and Defining Roles
    • Examples (few-shot prompting)
    • Iterative refinement by testing

    It doesn’t change the model itself, but the way we communicate with the model will be different.

    Key Points of Contrast between Prompt Engineering and Conventional Training

    1. Comparing Model Modification and Model Usage

    “Traditional training involves modifying the parameters of the model to optimize performance. Prompt engineering involves no modification of the model—only how to better utilize what knowledge already exists within it.”

    2. Data and Resource Requirements

    Model training involves extensive data, human labeling, and costly infrastructure. Contrast this with prompt design, which can be performed at low cost with minimal data and does not require training data.

    3. Speed and Flexibility

    Model training and retraining can take several days or weeks. Prompt engineering enables instant changes to the behavioral pattern through changes to the prompt and thus is highly adaptable and amenable to rapid experimentation.

    4. Skill Sets Involved

    “Traditional training involves special knowledge of statistics, optimization, and machine learning paradigms. Prompt engineering stresses the need for knowledge of the field, clarifying messages, and structuring instructions in a logical manner.”

    5. Scope of Control

    Training the model allows one to have a high, long-term degree of control over the performance of particular tasks. It allows one to have a high, surface-level degree of control over the performance of multiple tasks.

    Why Prompt Engineering has Emerged to be So Crucial

    The emergence of large general-purpose models has changed the dynamics for the application of AI in organizations. Instead of training models for different tasks, a team can utilize a single highly advanced model using the prompt method. The trend has greatly eased the adoption process and accelerated the pace of innovation,

    Additionally, “prompt engineering enables scaling through customization,” and various prompts may be used to customize outputs for “marketing, healthcare writing, educational content, customer service, or policy analysis,” through “the same model.”

    Shortcomings of Prompt Engineering

    Despite its power, there are some boundaries of prompt engineering. For example, neither prompt engineering nor any other method can teach the AI new information, remove deeply set biases, or function correctly all the time. Specialized or governed applications still need traditional or fine-tuning approaches.

    Conclusion

    At a very conceptual level, training a traditional model involves creating intelligence, whereas prompt engineering involves guiding this intelligence. Training modifies what a model knows, whereas prompt engineering modifies how a certain body of knowledge can be utilized. In this way, both of these aspects combine to constitute methodologies that create contrasting trajectories in AI development.

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