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: 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: 27/12/2025In: ,

AI tool causes a clinical error

artificial intelligence regulationclinical decision support systemshealthcare law and ethicsmedical accountabilitymedical negligencepatient safety
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    AI in Healthcare: What Healthcare Providers Should Know Clinical AI systems are not autonomous. They are designed, developed, validated, deployed, and used by human stakeholders. A clinical diagnosis or triage suggestion made by an AI model has several layers before being acted upon. There is, thereRead more

    AI in Healthcare: What Healthcare Providers Should Know

    Clinical AI systems are not autonomous. They are designed, developed, validated, deployed, and used by human stakeholders. A clinical diagnosis or triage suggestion made by an AI model has several layers before being acted upon.

    There is, therefore, an underlying question:

    Was the damage caused by the technology itself, by the way it was implemented, or by the way it was used?

    The answer determines liability.

    1. The Clinician: Primary Duty of Care

    In today’s health care setup, health care providers’ decisions, even in those supported by AI, do not exempt them from legal liability.

    If a recommendation is offered by an AI and the following conditions are met by the clinician, then:

    • Accepts it without appropriate clinical judgment, or
    • Neglects obvious signs that go against the result produced by AI,

    So, in many instances, the liability may rest with the clinician. AI systems are not considered autonomous decision-makers but rather decision-support systems by courts.

    Legally speaking, the doctor’s duty of care for the patient is not relinquished merely because software was used. This is supported by regulatory bodies, including the FDA in the United States, which considers a majority of the clinical use of AI to be assistive, not autonomous.

    2. The Hospital or Healthcare Organization

    Healthcare providers can be held responsible for damage caused by system-level issues, for instance:

    • Lack of adequate training among staff
    • Poor incorporation of AI in clinical practices
    • Ignoring known limitations of the system or warnings about safety

    For instance, if an AI decision-support system is required by a hospital in terms of triage decisions but an accompanying guideline is lacking regarding under what circumstances an override decision by clinicians is warranted, then the hospital could be held jointly liable for any errors that occur.

    With the aspect of vicarious liability in place, the hospital can be potentially responsible for negligence committed through its in-house professionals utilizing hospital facilities.

    3. AI Vendor or Developer

    Under product liability or negligence, AI developers can be made responsible, especially if negligence occurs in relation to:

    • Inherently Flawed Algorithm/Design Issues in Models
    • Biased or poor quality training data
    • Lack of Pre-Deployment Testing
    • Lack of disclosure of known limitations or risks

    If an AI system is malfunctioning in a manner inconsistent with its approved use, market claims, legal liability could shift toward the vendor. This leaves developers open to legal liability in case their tools end up malfunctioning in a manner inconsistent with their approved use

    But vendors tend to mitigate any responsibility for liability by stating that the use of the AI system should be under clinical supervision, since it is advisory only. Whether this will be valid under any legal system is yet to be tested.

    4. Regulators & Approval Bodies (Indirect Role)

    The regulatory bodies are not responsible for liability pertaining to clinical mistakes, but regulatory standards govern liability.

    The World Health Organization, together with various regulatory bodies, is placing a mounting importance on the following:

    • Transparency and explainability
    • Human-in-loop decision making
    • Continuous monitoring of AI performance

    Non-compliance with legal standards may enhance the validity of legal action against hospitals or suppliers in the event of injuries.

    5. What If the AI Is “Autonomous”?

    This is where the law gets murky.

    This becomes an issue if an AI system behaves independently without much human interference, such as in cases of fully automated triage decisions or treatment choices. The existing liability mechanism becomes strained in this scenario because the current laws were never meant for software that can independently impact medical choices.

    Some jurists have argued for:

    • Contingent liability schemes
    • Mandatory Insurance for AI MitsuruClause Insurance for AI
    • New legal categorizations for autonomous medical technologies

    At least, in today’s world, most medical organizations do not put themselves at risk in this manner, as they do, in fact, mandate supervision by medical staff.

    6. Factors Judged by the Court for Errors Associated with AI

    In applying justice concerning harm caused by artificial intelligence, the courts usually consider:

    • Was the AI used for the intended purpose?
    • Was the practitioner prudent in medical judgment?
    • Was the AI system sufficiently tested and validated?
    • Were limitations well defined?
    • Was there proper training and governance in the organization?

    The absence or presence of AI may not be as crucial to liability but rather its responsible use.

    The Emerging Consensus

    The general world view is that AI does not replace responsibility. Rather, the responsibility is shared in the AI environment in the following ways:

    • Healthcare Organizations: Responsible for the governance & implementation
    • Suppliers of AI systems: liable for secure design and honest representation

    This shared responsibility model acknowledges that AI is not a value-neutral tool or an autonomous system it is a socio-technical system that is situated within healthcare practice.

    Conclusion

    Consequently, it is not only technology errors but also system errors. The issue of blame in assigning liability focuses not on pinning down whose mistake occurred but on making all those in the chain, from the technology developer to the medical practitioner, do their share.

    Until such time as laws catch up to define the specific role of autonomous biomedical AI, being responsible is a decidedly human task. There is no question about the best course in either safety or legal terms. Being human is the key. Keep the responsibility visible, traceable, and human.

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

“Adi Vaani,” being positioned as a to ...

adi vaaniai for social gooddigital preservationlanguage inclusionmultilingualtribal / indigenous languages
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     India's "Adi Vaani": Multilingual AI for Inclusion and Global Leadership Indeed, India's new multilingual AI system, "Adi Vaani," is being actively framed as an instrument of language inclusion as well as a demonstration of India's increasing stature in international AI development. This effort mirRead more

     India’s “Adi Vaani”: Multilingual AI for Inclusion and Global Leadership

    Indeed, India’s new multilingual AI system, “Adi Vaani,” is being actively framed as an instrument of language inclusion as well as a demonstration of India’s increasing stature in international AI development. This effort mirrors India’s desire to integrate technological innovation with cultural and linguistic diversity — something few nations undertake at scale.

    Bridging Linguistic Diversity

    India alone has more than 22 officially spoken languages and thousands of regional dialects, so digital inclusivity is a serious challenge. Most AI platforms today are extremely biased towards English or other world-major languages and leave millions of citizens un-served in their local languages.

    “Adi Vaani” is built to comprehend, create, and communicate in various Indian languages, from Hindi, Tamil, Bengali, and Marathi to less commonly spoken languages such as Santali, Dogri, or Manipuri. The AI has the potential to:

    • Translate words and speech in real-time
    • Create locally pertinent content
    • Support education, government services, and healthcare provision

    This places the AI as a bridge between humans and technology, so digital transformation would not exclude non-English speakers.

     India’s Global AI Leadership Ambitions

    Aside from local inclusion, “Adi Vaani” is also a representation of India’s desire to become a leader in global AI innovation. With the development of a model capable of addressing multiple languages, India is showcasing technological abilities that are:

    • Culturally sensitive: The AI honors context, idioms, and subtleties in Indian languages.
    • Ethically aligned: Efforts are underway to minimize biases and provide safe, unbiased outputs.
    • Collaboratively adaptable: It can be employed by global institutions wanting to extend multilingual AI solutions elsewhere in the world with linguistic diversity.

    By way of “Adi Vaani,” India takes on the mantle not only as a consumer of AI technology but also as a global leader, able to solve problems that cannot be solved by large monolingual models.

     Uses Across Industries

    The potential uses are broad:

    • Education: Offering learning material in local languages, enabling children and adults to access quality material.
    • Governance: Enabling interaction between government services and citizenry who communicate in minority languages.
    • Healthcare: Providing AI-based telemedicine solutions and knowledge in local languages.
    • Business & Media: Facilitating content generation, marketing, and customer support on various linguistic markets.

    This renders “Adi Vaani” both a technological intervention and a social inclusion program.

    Challenges and Next Steps

    Surely, scaling a multilingual AI also poses challenges:

    • Scarcity of data for smaller languages
    • Sustaining accuracy and subtlety
    • Avoiding biases and harmful content

    Indian scientists are said to be merging government data sets, local studies, and community feedback to tackle these challenges. Furthermore, ethical frameworks are being prioritized in order to make the AI respect privacy, culture, and societal norms.

    A Step Towards Inclusive AI

    In reality, “Adi Vaani” is not just an AI model — it’s a mission statement. India is making a promise that it can excel in spaces where world technology leaders struggle, most importantly, inclusivity, cultural understanding, and practical impact.

    By combining technological capability with language diversity, India is looking to build an AI environment that’s globally competitive but locally empowering.

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daniyasiddiquiEditor’s Choice
Asked: 11/10/2025In:

a country improve its terms of trade

international tradelarge country assumptiontariffsterms of tradetrade policywelfare economics
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

     What "Terms of Trade" Actually Is Terms of trade (ToT) quantify the value of a nation's exports in relation to its imports. Simply put, it is the rate at which you exchange what you sell to the world for what you purchase from it. Terms of Trade  Export Prices Import Prices Terms of Trade Import PrRead more

     What “Terms of Trade” Actually Is

    Terms of trade (ToT) quantify the value of a nation’s exports in relation to its imports. Simply put, it is the rate at which you exchange what you sell to the world for what you purchase from it.
    Terms of Trade 
    1. Export Prices
    2. Import Prices
    3. Terms of Trade
    4. Import Prices
    5. Export Prices
    If your prices for exporting are higher or your prices for importing are lower, your terms of trade are better — i.e., you can purchase more imports with the same number of exports.
    Increasing your terms of trade is essentially negotiating a better bargain in international trade — you pay less and receive more. All countries would be happy about that.

     The Theory: The “Optimal Tariff” Argument

    That’s where economics comes in with the concept of the optimal tariff — an idea that goes back to the early 20th century, with economists such as Bickerdike and Johnson.
    The thinking is this:
    • Assume your nation is big enough in global trade to make a difference in world prices (such as the U.S., EU, or China).
    • You put a tariff on imports — 10%, for example.
    • Foreign exporters have increased obstacles to selling into your market.
    • To maintain their commodities competitive, they may reduce their export prices.
    If that is the case, your nation pays less for imports, but your exports remain at about the same price.

    Your terms of trade are better.

    In this case, some of the burden of the tariff is placed on foreign producers instead of your domestic consumers. You receive better prices from overseas, and the revenue from the tariff contributes to your national income.
    In the theoretical economic world alone, that’s a win-win — at least for your nation.

    Why It Only Works for “Large” Economies

    The important assumption here is that the nation has market power — the capacity to influence world prices.
    • A small economy (such as Nepal or Costa Rica) can’t; world prices are determined by much bigger markets. Any tariff it levies simply increases local prices and penalizes its own citizens.
    • A big economy (such as the U.S., China, or the EU) can shape world demand sufficiently that foreign producers may pass on some of the tariff by reducing prices.

    That’s why this concept is referred to as the “optimal tariff” — it’s the tariff that optimizes the welfare of a country by enhancing its terms of trade just sufficient to cover the loss of efficiency from restricting trade.

    But There’s a Catch: Retaliation

    In real life, the world economy is not a game with one player. When one large nation applies tariffs, others retaliate.
    • This reprisal negates any initial gain due to improved terms of trade and usually leads to a trade war, lowering world welfare for all.
    • Throughout the U.S.–China trade war (2018–2020), both countries applied tariffs to shield their own industries and enhance bargaining leverage.
    • Rather than enhancing terms of trade, both countries incurred greater import prices, dislocated supply chains, and reduced growth.
    • Economists subsequently calculated the alleged “gains” from better trade terms as entirely offset by losses to consumers and exporters.
    So, theory may tell us that an optimal tariff makes things better, but the reality is that retaliation murders the gain.

    Contemporary Complexity: Global Value Chains

    One other reason the theory falls apart today is the nature of contemporary trade.
    • Years ago, nations primarily exchanged finished goods: one country sold cars, another textiles. Nowadays, production is splintered across borders — a product can travel 5–6 countries before it is delivered to consumers.
    • Placing a tariff on “imports” usually means levying taxes on components and materials your industries require. That increases costs for manufacturers at home, undermines exports, and can deteriorate your terms of trade instead of enhancing them.
    So, something that could have succeeded in the 1950s no longer works for the highly interdependent 2025 world economy.

     The Human Angle: Winners and Losers

    Even in theory, when a nation improves its national terms of trade by raising a tariff, not all are winners.
    • Consumers pay more — they lose purchasing power.
    • Protected industries win in the short term, with less foreign competition.
    • Exporters usually lose when trading nations retaliate.
    Poor families will hurt the most, as tariffs usually target first imported necessities (fuel, food, or technology).
    So, although the country’s overall well-being may appear healthier on paper, the effects on distribution can prove to be politically charged.

    Historical Examples

    The American Smoot-Hawley Tariff Act (1930): Meant to defend American farmers and enhance terms of trade, it actually unleashed a worldwide retaliation that further exacerbated the Great Depression.
    The U.S.–China Tariffs (2018–2020): Designed to better America’s trade position, they increased consumer prices and damaged manufacturing exports. Analysis concluded that there was nearly no net gain in U.S. terms of trade after allowing for retaliation.
    India’s selective import tariffs in recent years demonstrate that low, sector-specific duties can short-term spur domestic production, but the overall benefits are frequently balanced by more expensive imports and reduced export growth.

    In Summary

    So, can a nation enhance its terms of trade by raising a tariff?
    In theory, yes — if it’s a large economy, if the tariff is small, and if other countries don’t retaliate.
     In practice, nearly never — because international interdependence and political reaction undo those gains.
    The reality is:
    Tariffs are like painkillers — they may provide temporary relief, but excessive use creates greater long-term harm.
    Whereas a wisely calibrated tariff could temporarily adjust trade terms to benefit a dominant country, consumer welfare, global trust, and economic efficiency costs are typically far greater than the gains. Cooperation and open trade continue to be the longer-run run more sustainable way to raise welfare and prosperity in today’s global economy.
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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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Asked: 09/08/2025In: ,

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  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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