AI tool causes a clinical error
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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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:
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:
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:
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:
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:
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:
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:
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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