mohdanasMost Helpful
Asked: 13/09/2025In:

cultural references or word choices

language
  1. Anonymous
    Anonymous

    1. Idioms and Expressions Native speakers make extensive use of idioms, slang, and brief "throwaway" phrases that don't literally fit. For example: A native would say: "That movie was a total flop." A fluent non-native would say: "That movie was not successful." Both are fine, but the second soundsRead more

    1. Idioms and Expressions

    Native speakers make extensive use of idioms, slang, and brief “throwaway” phrases that don’t literally fit. For example:

    • A native would say: “That movie was a total flop.”
    • A fluent non-native would say: “That movie was not successful.”

    Both are fine, but the second sounds a little formal. It’s not wrong — it just doesn’t have the casual, cultural shorthand that natives pick up.

    2. Pop Culture References

    Natives have a habit of inserting TV, movie, sports, or music quotes without thinking. For example:

    • Using “It’s my kryptonite” (Superman) to mean “my weakness.”
    • Or “That’s a slam dunk” (basketball) to mean “an easy win.”

    Unless you regularly use (or even recognize) those references, you’ll be perfectly comprehensible but a bit “outside” the shared cultural bubble.

    3. Word Register and Context

    Sometimes learners choose a word that is technically correct but not the one natives would use in casual speech. For example:

    • Non-native: “I am very fatigued.”
    • Native: “I’m so tired.”

    Or:

    • Non-native: “We must commence the meeting.”
    • Native: “Let’s get started.”

    It’s not that your English is wrong — it’s just too polished for the situation. Natives notice the mismatch between the register (formal vs. casual) and the context.

    4. Politeness and Directness

    • Cultural norms rule how we sugarcoat requests or how we refuse.
    • Natives use these sentences in English: “Could you maybe open the window?” or “I don’t know if this will work, but…”
    • A fluent learner would say: “Open the window.” or “This won’t work.”
    • Grammatically correct, but the tone sounds brusque because there is no “politeness padding.” These tiny social nuances are extremely cultural.

    5. Literal Thinking vs. Metaphorical Thinking

    There are metaphors galore in English: “time flies,” “spill the tea,” “hit the road.” Non-natives explain things in a more literal way: “time passes quickly,” “tell gossip,” “begin the trip.” True and to the point, but lacking the playful, metaphor-laden flavor that natives use naturally.

    6. Small Talk Topics

    Even what is discussed will sound foreign. For example, in some cultures, individuals dive into serious subjects immediately. In English-speaking countries, small talk is virtually ritual:

    1. Weather (“Crazy rain today, huh?”)
    2. Sports (“Did you watch the game?”)
    3. Weekend plans (“Got anything exciting planned?”)

    If you don’t do this or don’t tread too heavily right away, natives will be able to sense that you’re “not from around here” even if your English is impeccable.

    7. Over-Explaining or Under-Explaining

    Accuracy is valued in some cultures, and the students will therefore give long, accurate answers:

    Q: “How are you?”

    • Non-native: “I am a little bit tired because I did not sleep very well, but otherwise all right.”
    • Native: “I’m good, thanks. You?”

    The long answer is absolutely correct, but sounds odd in informal English where short, habitual replies are typical.

     The Bottom Line

    Even if your English is silky, word choice and cultural references function as little road signs of where you’re from. It’s not a defect — it just means your voice has a different rhythm of culture. Fluency will get you heard; cultural subtlety will get you in.

    And here’s where the good news comes in: occasionally sounding “foreign” is beneficial. People remember your new ways of phrasing things, your fresh take on things, and they call you back for it. You don’t have to compromise who you are in order to become fluent — you get to decide how much you can accommodate.

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daniyasiddiquiEditor’s Choice
Asked: 08/12/2025In:

global markets enter a recession in 2

economicforecastglobaleconomymacroeconomicsmarketoutlookrecession2025softlanding
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. What do “recession” and “soft landing” actually mean? Before we talk predictions, it helps to clear up the jargon: Global recession (in practice) means: World growth drops to something like ~1–2% or less. Several major regions (US, Euro area, big emerging markets) are in outright contraction forRead more

    1. What do “recession” and “soft landing” actually mean?

    Before we talk predictions, it helps to clear up the jargon:

    Global recession (in practice) means:

    • World growth drops to something like ~1–2% or less.
    • Several major regions (US, Euro area, big emerging markets) are in outright contraction for a while.
    • Unemployment rises clearly, trade slows sharply, corporate earnings fall, defaults rise.

    Soft landing means:

    Central banks managed to tame inflation by raising rates…

    • …without “breaking” the economy.
    • Growth slows but stays positive. Some sectors hurt, some countries stagnate, but the world as a whole doesn’t fall into an outright slump.

    The current debate is really:

    “Do we get a long, uncomfortable slowdown that we can live with, or does something snap and push us into a real global downturn?”

    2. What are the official forecasts saying right now?

    If you look at the big global institutions, their base case is “slow, fragile growth” rather than “clear recession”:

    • The IMF’s October 2025 World Economic Outlook projects global growth of about 3.2% in 2025 and 3.1% in 2026 weaker than pre-COVID norms, but still growth, not contraction.

    • The World Bank is more pessimistic: their 2025 projections show global growth slowing to roughly the weakest pace since 2008 outside of official recessions, around the low-2% range.

    • The UN’s 2025 outlook also expects global growth to slow to about 2.4% in 2025, down from 2.9% in 2024.

    • The OECD (rich-country club) says global growth is “resilient but slowing”, supported by AI investment and still-decent labour markets, but with rising risks from tariffs and potential corrections in overvalued markets. 

    Think of it like this:

    • Nobody is forecasting a great boom.

    • Most are not forecasting an official global recession either.

    • The world is muddling through at an “OK but below-par” pace.

    3. But what about risk? Could 2025 still tip into recession?

    Yes. Quite a few serious people think the probability is non-trivial:

    • J.P. Morgan, for example, recently estimated about a 40% probability that the global or US economy will be in recession by the end of 2025. 

    • A McKinsey survey (Sept 2025) found that over half of executives picked one of two recession scenarios as the most likely path for the world economy in 2025 26. 

    So the base case is “soft landing or slow growth”, but there is a real coin-flip-ish risk that something pushes us over into recession.

    4. Why a soft landing still looks slightly more likely

    Here are the forces supporting the “no global crash” scenario:

    a) Growth is weak, but not dead

    • The IMF, World Bank, OECD, and others all have positive growth numbers for 2025 26.

    • Some major economies for example, the US and India are still expected to grow faster than the global average, helped by AI investment, infrastructure, and relatively strong labour markets. 

    This is not a booming world, but it is also not a shutdown world.

    b) Inflation is cooling, giving central banks more room

    • After the post-COVID spike, inflation in most large economies has been falling towards central bank targets. The OECD expects G20 inflation to gradually move towards ~2 3% by 2027. 

    • That allows central banks (like the Fed, ECB, RBI, etc.) to stop hiking and, in some cases, start cutting rates gradually, which reduces pressure on businesses and borrowers.

    In practical terms: mortgages, corporate borrowing, and EM currencies are now under less stress than at peak-rate times.

    c) Labour markets are bending, not collapsing

    • Unemployment has ticked up in some economies, but most big players still have reasonably strong labour markets, especially compared to pre-2008 crises.

    • When people keep jobs, they keep spending something, which supports earnings and tax revenue.

    d) Policy makers are terrified of a hard landing

    Governments and central banks remember 2008 and 2020. They know what a synchronized global crash looks like. That means:

    • Faster use of fiscal support (targeted transfers, investment incentives, etc.).

    • Central banks ready to react if markets seize up (swap lines, liquidity measures, etc.).

    Is it perfect? No. But the “lesson learned” effect reduces the odds of a completely uncontrolled collapse.

    5. What could still push us into a global recession?

    Now the uncomfortable part: the list of things that could go wrong is long.

    a) High interest rates + high debt = slow-burn risk

    • Even as inflation falls, real rates (inflation-adjusted) are higher than in the 2010s.

    • Governments, companies, and households rolled up a lot of debt over the past decade.

    • The IMF has flagged the rising cost of debt servicing and large refinancing needs as a major vulnerability. 

    A big refinancing wave at still-elevated rates could quietly choke weaker firms, banks, or even countries leading to defaults, financial stress, and eventually recession.

    b) Asset bubbles, especially in AI stocks and gold

    • The Bank for International Settlements (BIS) recently warned about a rare “double bubble”: both global stocks and gold are showing explosive price behaviour, driven partly by AI hype and central-bank gold buying.

    If equity markets (especially AI-heavy indices) correct sharply, it could hit:

    • Household wealth
    • Corporate borrowing costs
    • Confidence in the real economy

    The Economist has even outlined how a market-driven downturn might look: not necessarily as deep as 2008, but still enough to push the world into a mild recession.

    c) Trade wars, tariffs, and geopolitics

    • The OECD’s latest outlook explicitly notes that new tariffs and trade tensions, especially involving the US and China, are a meaningful downside risk for global growth.

    Add on top:

    • Middle East tensions affecting energy prices
    • War impacts on Europe and supply chains
    • Rising protectionism in multiple regions

    Any major escalation could hit trade, energy costs, and confidence very quickly.

    d) China’s structural slowdown

    China is still targeting around 5% growth, but:

    • It faces a deep property slump, weak domestic demand, and shifting export patterns. 

    • If Beijing mis-handles the delicate balance between stimulus and reform, China’s slowdown could be sharper dragging down commodity exporters, Asian neighbours, and global trade.

    e) “Running hot” for too long

    Some rich countries are still running relatively loose fiscal policy, even with high debt and not-yet-normal inflation. Reuters described it as the world economy being “run hot” good for growth now, but potentially risky for future inflation, bond markets, and currency stability.

    If bond markets suddenly demand higher yields, you can get a shock similar to the UK’s mini-budget crisis in 2022 but scaled up.

    6. So what does this mean in real life, for normal people?

    If the base case (soft landing / weak growth) plays out, 2025 26 will probably feel like:

    • Slow but not catastrophic:

    Growth is there, but it feels “meh”.

    Salary hikes and hiring are slower, but most people keep their jobs.

    • Sector splits:

    AI/tech, defence, some infrastructure and energy plays could remain strong.

    Rate-sensitive sectors (real estate, some consumer discretionary) stay under pressure.

    • High volatility:

    Markets jump on every inflation print, Fed/ECB statement, or geopolitical headline.

    Short-term traders may love it; long-term investors feel constantly nervous.

    If the risk case (recession) hits, it will likely show up as:

    • A sharp equity correction (especially in AI-rich indices).

    • A rush into “safe” assets (bonds, gold, defensive sectors).

    • Rising defaults in riskier debt and weaker economies.

    • Rising unemployment and profit cuts.

    7. How should an investor think about this (without pretending to predict the future)?

    I cannot and should not tell you what to buy or sell that has to be tailored to your situation. But conceptually, given this backdrop:

    Do not bet your entire portfolio on one macro view.

    Assume both:

    • Scenario A: slow, choppy soft landing; and
    • Scenario B: a mild-to-moderate recession
      are reasonably plausible, and stress-test your allocations against both.

    Watch your leverage.

    • High-rate environments + volatile markets are where over-leveraged traders get wiped out first.

    Quality matters more when the tide goes out.

    • Strong balance sheets
    • Stable cash flows
    • Reasonable valuations

    tend to survive both soft landings and recessions better than speculative names that only work in a perfect world.

    Diversify across regions and asset classes.

    • The US, Europe, China, India, and EMs will not move in perfect sync.
    • Mixing equities, high-quality bonds, and maybe some alternatives can make you less dependent on a single macro outcome.

    Time horizon is your friend.

    If your horizon is 7–10+ years, the exact label “recession” vs “soft landing” in 2025 matters less than:

    • Whether you avoid permanent capital loss
    • Whether you steadily accumulate quality assets at reasonable prices

    Bottom line

    If you force me to put it in one sentence:

    As of late 2025, the world is more likely to see an uncomfortably slow “soft landing” than a classic global recession but the runway is bumpy, and the probability of a downturn is high enough that no serious investor should ignore it.

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daniyasiddiquiEditor’s Choice
Asked: 14/11/2025In:

we moving towards smaller, faster, do ...

aiaitrendsllmsmachinelearningmodeloptimizationsmallmodels
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    1. The early years: Bigger meant better When GPT-3, PaLM, Gemini 1, Llama 2 and similar models came, they were huge.The assumption was: “The more parameters a model has, the more intelligent it becomes.” And honestly, it worked at first: Bigger models understood language better They solved tasks morRead more

    1. The early years: Bigger meant better

    When GPT-3, PaLM, Gemini 1, Llama 2 and similar models came, they were huge.
    The assumption was:

    “The more parameters a model has, the more intelligent it becomes.”

    And honestly, it worked at first:

    • Bigger models understood language better

    • They solved tasks more clearly

    • They could generalize across many domains

    So companies kept scaling from billions → hundreds of billions → trillions of parameters.

    But soon, cracks started to show.

    2. The problem: Giant models are amazing… but expensive and slow

    Large-scale models come with big headaches:

    High computational cost

    • You need data centers, GPUs, expensive clusters to run them.

    Cost of inference

    • Running one query can cost cents too expensive for mass use.

     Slow response times

    Bigger models → more compute → slower speed

    This is painful for:

    • real-time apps

    • mobile apps

    • robotics

    • AR/VR

    • autonomous workflows

    Privacy concerns

    • Enterprises don’t want to send private data to a huge central model.

    Environmental concerns

    • Training a trillion-parameter model consumes massive energy.
    • This pushed the industry to rethink the strategy.

    3. The shift: Smaller, faster, domain-focused LLMs

    Around 2023–2025, we saw a big change.

    Developers realised:

    “A smaller model, trained on the right data for a specific domain, can outperform a gigantic general-purpose model.”

    This led to the rise of:

     Small models (SMLLMs) 7B, 13B, 20B parameter range

    • Examples: Gemma, Llama 3.2, Phi, Mistral.

    Domain-specialized small models

    • These outperform even GPT-4/GPT-5-level models within their domain:

    • Medical AI models

    • Legal research LLMs

    • Financial trading models

    • Dev-tools coding models

    • Customer service agents

    • Product-catalog Q&A models

    Why?

    Because these models don’t try to know everything they specialize.

    Think of it like doctors:

    A general physician knows a bit of everything,but a cardiologist knows the heart far better.

    4. Why small LLMs are winning (in many cases)

    1) They run on laptops, mobiles & edge devices

    A 7B or 13B model can run locally without cloud.

    This means:

    • super fast

    • low latency

    • privacy-safe

    • cheap operations

    2) They are fine-tuned for specific tasks

    A 20B medical model can outperform a 1T general model in:

    • diagnosis-related reasoning

    • treatment recommendations

    • medical report summarization

    Because it is trained only on what matters.

    3) They are cheaper to train and maintain

    • Companies love this.
    • Instead of spending $100M+, they can train a small model for $50k–$200k.

    4) They are easier to deploy at scale

    • Millions of users can run them simultaneously without breaking servers.

    5) They allow “privacy by design”

    Industries like:

    • Healthcare

    • Banking

    • Government

    …prefer smaller models that run inside secure internal servers.

    5. But are big models going away?

    No — not at all.

    Massive frontier models (GPT-6, Gemini Ultra, Claude Next, Llama 4) still matter because:

    • They push scientific boundaries

    • They do complex reasoning

    • They integrate multiple modalities

    • They act as universal foundation models

    Think of them as:

    • “The brains of the AI ecosystem.”

    But they are not the only solution anymore.

    6. The new model ecosystem: Big + Small working together

    The future is hybrid:

     Big Model (Brain)

    • Deep reasoning, creativity, planning, multimodal understanding.

    Small Models (Workers)

    • Fast, specialized, local, privacy-safe, domain experts.

    Large companies are already shifting to “Model Farms”:

    • 1 big foundation LLM

    • 20–200 small specialized LLMs

    • 50–500 even smaller micro-models

    Each does one job really well.

    7. The 2025 2027 trend: Agentic AI with lightweight models

    We’re entering a world where:

    Agents = many small models performing tasks autonomously

    Instead of one giant model:

    • one model reads your emails

    • one summarizes tasks

    • one checks market data

    • one writes code

    • one runs on your laptop

    • one handles security

    All coordinated by a central reasoning model.

    This distributed intelligence is more efficient than having one giant brain do everything.

    Conclusion (Humanized summary)

    Yes the industry is strongly moving toward smaller, faster, domain-specialized LLMs because they are:

    • cheaper

    • faster

    • accurate in specific domains

    • privacy-friendly

    • easier to deploy on devices

    • better for real businesses

    But big trillion-parameter models will still exist to provide:

    • world knowledge

    • long reasoning

    • universal coordination

    So the future isn’t about choosing big OR small.

    It’s about combining big + tailored small models to create an intelligent ecosystem just like how the human body uses both a brain and specialized organs.

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

new tariff measures slow down the glo ...

2026 economic forecasteconomic slowdownglobal economic recoverysupply chainstariffstrade barriers
  1. daniyasiddiqui
    daniyasiddiqui Editor’s Choice

    Why tariffs matter for a fragile recovery (the mechanics, in plain English) Tariffs raise prices for businesses and consumers. When a government imposes a tariff on an imported input or finished product, importers and domestic purchasers generally end up paying higher — either because the tariff getRead more

    Why tariffs matter for a fragile recovery (the mechanics, in plain English)

    Tariffs raise prices for businesses and consumers.

    When a government imposes a tariff on an imported input or finished product, importers and domestic purchasers generally end up paying higher — either because the tariff gets translated into higher consumer prices, or because companies swallow reduced margins and reduce other expenses. That diminishes consumers’ buying power and companies’ investment capacity. (Consider it a new tax on the wheels of commerce.)

    They upend supply chains and inject uncertainty.

    Contemporary manufacturing is based on parts from numerous nations. Novel tariffs — particularly those imposed suddenly or asymmetrically — compel companies to redirect supply chains, create new inventory buffers, or source goods at greater cost. That slows down manufacturing, postpones investment and even leads factories to sit idle as substitutes are discovered.

    They squeeze investment and hiring.

    High policy risk causes companies to delay capital spending and recruitment. Even if demand is fine at the moment, companies won’t invest if they can’t forecast future trade prices or access to markets.

    They can fuel inflation and encourage tighter policy.

    Price increases due to tariffs fuel inflation. If central banks react by maintaining higher interest rates for longer, that will crimp demand and investment — a double blow for a recovery that relies on cheap credit.

    All of these channels push against one another and against the forces attempting to boost growth (fiscal stimulus, reopening post-pandemic, tech spending). The net impact hinges on how big and sustained the tariffs are. The IMF and OECD maintain the risk is real.

    What the numbers and forecasters are saying (summary of the latest views)

    • Higher tariffs and increased policy uncertainty have been warned by the OECD to lower global GDP growth significantly — forecasting a deceleration through to 2026 as front-loading effects dissipate and tariff pressures take hold. They openly attribute higher tariff levels to lower investment and trade volumes.
    • The WTO also forecasts world trade expansion to slow sharply in 2026 (merchandise trade expansion dropping to a soft pace), with tariff actions among the pressures bearing down on trade.
    • The IMF raised a warning that while growth remained resilient in 2025, a sustained rise in tariffs and policy uncertainty would “significantly slow world growth” if continued. Their World Economic Outlook identifies uncertainty and trade distortions as risks on the downside.

    In short: large institutions concur that the risk of tariffs hindering recovery is real — and newer analysis suggests a quantifiable downgrade in 2026 growth if tariffs are high and uncertainties are unresolved.

    Who suffers most — and who may escape relatively unharmed?

    Big losers:

    • Trade-dependent emerging economies (exporters of intermediate goods and commodity-linked producers) — since they experience lower demand and potential “green tariffs” or other restrictions from developed economies.
    • Global value-chain companies (autos, electronics, machinery) — since they depend on cross-border inputs and close timing.
    • Poor consumers in countries imposing tariffs — since consumer-goods tariffs are regressive (they increase prices for staples and products poorer households allocate a larger proportion of their budget towards).

    Less exposed:

    • Industrial sectors manufacturing domestic substitutes protected by protection (short term), even though that compromises on efficiency and increases economy-wide costs.
    • Countries or companies able to rapidly re-shore or diversify supply chains — but re-shoring requires time and money.
    • The distributional shock matters: even small overall GDP losses can mean more hurt to exposed regions and sectors. Historical experience in previous episodes of tariffs indicates that the gains for sheltered firms tend to be smaller and shorter-run than the economy-wide losses.

    Magnitude: how large could the impact be?

    Projections vary by scenario, but the consensus picture from the OECD/IMF/WTO group is the same:

    tariffs and trade tensions can trim tenths of a percentage point from world GDP growth — sufficient to turn a weak recovery into a significantly weaker year (OECD projections indicate stabilizing global growth from low-3% ranges to closer to 2.9% in 2026 assuming higher tariffs). Those tenths count — slower growth translates into fewer jobs, less investment, and more fiscal burden for most nations.

    (Practical implication: 0.3–0.5 percentage point loss worldwide isn’t an apocalypse — but it is significant, and it accumulates with other shocks such as energy or financial distress.)

    • Three realistic scenarios (simple, useful framing)
    • Soft-hit scenario (tariffs constrained, short-term):

    Tariff measures are transient, exporters and companies get used to it rapidly, supply-chain responses are moderate. Outcome:

    modest slowdown in trade expansion and mild restraint on GDP — recovery still occurs, but less strong than it might have been.

    Medium-hit scenario (extended, sector-targeted tariffs + uncertainty):

    Investment is postponed, tariffs are extended. Trade development comes to an end; some sectors retreat or regionalize. Recovery halts in 2026 and unemployment / under-employment persists above desired levels.

    Extreme scenario (large tit-for-tat tariffs + export controls):

    Large tariffs and export controls break up global supply chains (tech, strategic minerals, semiconductors). Investment and productivity suffer. Materially slower growth, persistent inflation pressures, and policymakers’ hard trade-off between supporting demand and resisting inflation. Recent action on export controls and trade measures makes this tail risk more realistic than it was last year.

    What do policymakers and companies do (adoption and mitigation)?

    Policy clarity and multilateral cooperation. Fast, open negotiation and application of WTO dispute-resolution or temporary exceptions can minimize uncertainty. Multilateral rules prevent mutually destructive tit-for-tat reprisals. The institutions (IMF/OECD/WTO) have been calling for clarity and cooperation.

    • Targeted fiscal support. If tariffs increase prices for poor households, targeted transfers or vouchers mute the welfare cost without extending protectionism.
    • Aid for diversifying supply chains. Government encouragement for diversifying inputs and constructing robust—but not excessively costly—regional networks can minimize exposure.
    • Private sector initiative. Companies can speed up diversification of procurement, enhance stock visibility, and re-train workforces for a marginally different manufacturing base.

    Bottom line — the people bit

    When individuals pose “will tariffs delay the recovery?

    “they’re essentially wondering whether the positive things we experienced coming back to after the pandemic — employment, regular paychecks, lower-cost smartphones and appliances — are in jeopardy.”. The facts and the largest global agencies agree, yes, it exists: tariffs increase costs, drain investment, and introduce uncertainty — all of which could convert a weak uplift into a flatter, more disappointing 2026 year for growth. How bad it is will depend on decisions:

    whether governments ratchet up or back off, whether companies respond quickly, and whether multilateral collaboration can be saved ahead of supply chains setting in permanent, less efficient forms. OECD

    If you’d like, I can:

    • Compile a brief, footnoted one-page summary with the exact OECD/IMF/WTO figures and dates; or
    • Run a targeted scenario projection for a specific country or industry (e.g., India manufacturing, EU steel, or world semiconductors) based on the latest tariff moves and trade ratios.
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daniyasiddiquiEditor’s Choice
Asked: 15/10/2025In: ,

design assessments in the age of AI

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

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

    How to Design Tests in the Age of AI

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

    So the challenge that educators today are facing is:

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

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

    1. Redefining What We’re Assessing

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

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

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

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

    This shifts the attention away from memorization.

     2. Creating “AI-Resilient” Tests

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

    Here are a few effective formats:

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

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

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

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

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

    Thinking activities:

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

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

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

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

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

    Example prompt:

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

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

     4. Immersing Technology with Human Touch

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

    Ideas:

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

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

     5. Justice and Integrity

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

    Teachers can promote equity by:

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

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

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

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

     6. Remixing Feedback in the AI Era

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

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

     7. From Testing to Learning

    The most powerful change can be this one:

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

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

    Teachers can:

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

    Final Thought

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

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

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daniyasiddiquiEditor’s Choice
Asked: 15/08/2025In:

Is walking 10,000 steps a day enough ...

health
  1. daniyasiddiqui
    Best Answer
    daniyasiddiqui Editor’s Choice

    Walking 10,000 steps a day is a good goal. Whether it’s “enough” depends on your health and fitness goals. For most people, reaching that number means you’re moving regularly. This improves heart health, boosts circulation, and keeps joints flexible. It can also help maintain a healthy weight, reducRead more

    Walking 10,000 steps a day is a good goal. Whether it’s “enough” depends on your health and fitness goals.

    For most people, reaching that number means you’re moving regularly. This improves heart health, boosts circulation, and keeps joints flexible. It can also help maintain a healthy weight, reduce stress, and provide a nice mental break from being outside or away from your desk. Research shows that even 7,000 to 8,000 steps a day can bring great health benefits, especially if you’ve lived a mostly sedentary lifestyle.

    That said, steps alone might not meet all your body’s needs. Walking is excellent for endurance and general wellness, but it doesn’t build much muscle or bone strength. For a complete fitness regime, it’s worth adding some strength training, stretching, or higher-intensity activities a few times a week.

    So yes, 10,000 steps is a solid daily habit for overall health. Think of it as your baseline for movement, not your full fitness routine.

    If you’d like, I can break down how many steps correspond to different levels of fitness so you can customize your goal.

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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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