AI Generated Exam Paper

A Level H1 General Paper Practice Paper 3

Free A Level H1 GP Practice Paper 3, LongCat AI version, with questions, answers, and A Level-style practice for Singapore students.

These static practice materials are generated from the site's syllabus and paper-generation workflow, with source and model context shown so students and parents can evaluate the material before use.

A Level H1 General Paper AI Generated Generated by LongCat 2.0 LLM Updated 2026-08-17

Back to Subject Browse Free Exam Papers

Questions

TuitionGoWhere Practice Paper - General Paper H1 A-Level

TuitionGoWhere Practice Paper (AI)

Subject: General Paper H1 Level: A-Level Paper: Practice Paper — Comprehension (Paper 2 Style) Duration: 1 hour 30 minutes Total Marks: 50 Name: ___________________________ Class: ___________________________ Date: ___________________________

Instructions

Read the passage carefully before attempting the questions.
Answer all questions.
For short-answer questions, use your own words as far as possible unless otherwise stated.
For the summary question, write your answer in continuous prose. Do not exceed the stated word limit.
For the application question, relate your answer to the context given and support your response with evidence from the passage.
Marks for each question are indicated in brackets [ ].
The total marks for this paper is 50.

Section A: Short-Answer Questions [35 marks]

Read the passage below and answer Questions 1–15.

The Rise of Artificial Intelligence in Modern Healthcare

The integration of artificial intelligence (AI) into healthcare has accelerated at a pace that few could have anticipated a decade ago. From diagnostic imaging to drug discovery, AI systems are now capable of performing tasks that were once the exclusive domain of trained medical professionals. Proponents argue that this technological revolution promises to democratise healthcare, making high-quality diagnosis and treatment accessible to populations that have long been underserved. Yet, as with all disruptive technologies, the rapid adoption of AI in medicine raises profound ethical, practical, and societal questions that demand careful scrutiny.

At the heart of the AI healthcare revolution lies machine learning — a subset of AI in which algorithms learn patterns from vast datasets without being explicitly programmed for each task. In radiology, for instance, deep learning models have demonstrated the ability to detect certain cancers on imaging scans with accuracy that rivals, and in some cases surpasses, that of experienced radiologists. A landmark study published in The Lancet Digital Health in 2023 found that an AI system detected breast cancer from mammograms with a sensitivity of 94.5%, compared to 88.0% for the average radiologist. Such findings have fuelled optimism that AI could help address the global shortage of diagnostic specialists, particularly in low- and middle-income countries where the ratio of radiologists to patients can be as low as 1 per million.

However, the enthusiasm surrounding AI diagnostics must be tempered by a sobering reality: these systems are only as good as the data on which they are trained. If training datasets are skewed — for example, if they underrepresent certain ethnic groups, age brackets, or disease subtypes — the resulting algorithms may perform poorly for those very populations that stand to benefit most from AI-assisted healthcare. A 2022 investigation by researchers at MIT revealed that several commercially available AI diagnostic tools exhibited significantly lower accuracy when analysing medical images from patients of African and South Asian descent, precisely because these groups were underrepresented in the training data. This phenomenon, known as "algorithmic bias," poses a serious threat to health equity and could, paradoxically, widen existing disparities rather than narrow them.

Beyond diagnostics, AI is also transforming the pharmaceutical industry. Traditional drug development is notoriously slow and expensive, often requiring over a decade and billions of dollars to bring a single drug to market. AI-driven platforms can dramatically compress this timeline by predicting how different molecular compounds will interact with biological targets, thereby identifying promising drug candidates in a fraction of the time. In 2024, the first AI-designed drug entered Phase III clinical trials — a milestone that many in the industry regard as a watershed moment. Yet critics caution that speed should not come at the expense of safety, and that the regulatory frameworks governing AI-assisted drug development remain woefully inadequate.

The ethical dimensions of AI in healthcare extend well beyond questions of bias and safety. One of the most contentious issues is the question of accountability. When an AI system makes an incorrect diagnosis that leads to patient harm, who bears responsibility? Is it the hospital that deployed the system, the developer who designed the algorithm, or the clinician who relied on its output? Current legal frameworks are ill-equipped to address this question, and the lack of clear accountability mechanisms could erode public trust in AI-assisted medicine. Furthermore, the increasing reliance on AI raises concerns about the dehumanisation of healthcare. Medicine has always been as much an art as a science, rooted in the empathetic relationship between doctor and patient. If algorithms begin to supplant human judgement in clinical decision-making, there is a risk that the human touch — the ability to listen, to comfort, to understand the patient as a whole person — may be diminished.

There is also the matter of data privacy. AI systems require enormous volumes of patient data to function effectively, and this data is often highly sensitive. While regulations such as the European Union's General Data Protection Regulation (GDPR) and Singapore's Personal Data Protection Act (PDPA) provide some safeguards, the sheer scale of data collection required for AI training creates vulnerabilities. Data breaches in the healthcare sector have become increasingly common, with millions of patient records exposed in high-profile cyberattacks in recent years. The prospect of sensitive health data being commodified or exploited by third parties is a legitimate concern that policymakers must address with urgency.

Despite these challenges, it would be a mistake to view AI in healthcare as a net negative. The technology holds genuine promise for improving patient outcomes, reducing costs, and expanding access to care. The key lies in responsible implementation — ensuring that AI systems are rigorously tested for bias, that robust regulatory frameworks are established, and that the technology is deployed as a complement to, rather than a replacement for, human expertise. As Dr. Sarah Chen, a bioethicist at the National University of Singapore, aptly observes: "AI will not replace doctors, but doctors who use AI will replace those who do not." The challenge for society is to ensure that this transition is managed equitably, transparently, and with the welfare of patients as the paramount concern.

Questions

1. The word "democratise" (line 3) suggests that AI in healthcare aims to ________________.

Explain what the author means by the use of this word in the context of the passage. [2]

2. Explain the author's use of the phrase "exclusive domain" (line 4) in the context of the passage. [2]

3. What does the author mean by describing AI's adoption in medicine as "rapid" (line 6)? How does this description shape the reader's understanding of the issues discussed in the passage? [2]

4. In paragraph 2, the author cites a study from The Lancet Digital Health (lines 12–14). Why does the author include this specific piece of evidence? [2]

5. Explain the phrase "tempered by a sobering reality" (line 17) as used in the passage. [2]

6. (a) What is "algorithmic bias" (line 22) as described in the passage? [2]

(b) Explain why the author uses the word "paradoxically" (line 23) in relation to algorithmic bias. [1]

7. In paragraph 3, the author describes drug development as "notoriously slow and expensive" (line 27). What effect does this word choice have on the reader's perception of AI's role in pharmaceuticals? [2]

8. What does the author mean by "a watershed moment" (line 31) in the context of AI-designed drugs entering Phase III trials? [2]

9. Explain the author's use of the phrase "woefully inadequate" (line 33) to describe regulatory frameworks. What does this reveal about the author's attitude? [2]

10. In paragraph 4, the author poses a series of questions about accountability (lines 36–38). Why does the author structure this section as a series of questions rather than statements? [2]

11. What does the author mean by "the dehumanisation of healthcare" (line 40)? Explain with reference to the passage. [2]

12. Explain the phrase "the human touch" (line 43) as used by the author. Why does the author consider this important? [2]

13. In the final paragraph, the author quotes Dr. Sarah Chen: "AI will not replace doctors, but doctors who use AI will replace those who do not" (lines 54–55). What is the purpose of including this quotation? [2]

14. Identify the author's overall tone in the passage. Provide two pieces of evidence from the text to support your answer. [3]

15. The passage discusses both the benefits and risks of AI in healthcare. In your own words, summarise the author's argument about why responsible implementation is crucial. Use information from the final paragraph only. [4]

Section B: Summary Question [8 marks]

Read the passage again and answer Question 16.

16. Summarise the concerns and challenges associated with the use of AI in healthcare, as presented in the passage.

You should write your summary as one continuous paragraph of no more than 120 words. Use your own words as far as possible. [8]

Section C: Application Question [7 marks]

Read the passage again and answer Question 17.

17. The Ministry of Health in Singapore is considering a proposal to integrate AI diagnostic tools into all public polyclinics by 2028. The proposal includes the following key features:

AI systems will be used as a first-line screening tool for common conditions such as diabetes-related eye disease and certain cancers.
All AI recommendations will be reviewed by a qualified doctor before any treatment is prescribed.
Patient data used to train AI systems will be anonymised and stored on secure government servers.
A new AI Ethics Board will be established to oversee the deployment and monitor outcomes.

(a) Using ideas from the passage, explain two concerns that the public might have about this proposal. Support your answer with evidence from the passage. [4]

(b) Which one feature of the proposal do you think best addresses the concerns raised in the passage? Justify your choice with reference to the passage. [3]

— End of Paper —

Free quiz and exam paper access

Enter your details to view this paper

Your access is remembered on this device.

Answers

TuitionGoWhere Practice Paper — General Paper H1 A-Level

Answer Key & Marking Scheme

Paper: Practice Paper — Comprehension (Paper 2 Style) Total Marks: 50

Section A: Short-Answer Questions [35 marks]

Question 1 [2 marks]

Answer: The word "democratise" suggests that AI in healthcare aims to make high-quality diagnosis and treatment available to everyone, including populations that have previously been underserved or lacked access to specialist medical care. The author uses this word to convey the idea that AI has the potential to level the playing field in healthcare access.

Marking Scheme:

1 mark for identifying the idea of making healthcare accessible to all / wider populations.
1 mark for linking it to underserved populations or the idea of equalising access.

Common Mistakes:

Students may simply define "democratise" in a political sense (relating to democracy/government) without connecting it to healthcare access.
Award only 1 mark if the answer is too vague (e.g., "to help more people") without reference to underserved populations.

Question 2 [2 marks]

Answer: The phrase "exclusive domain" means that certain medical tasks were previously performed only by trained medical professionals and no one else. The author uses this phrase to emphasise how significant the shift is — AI is now capable of doing work that was once restricted solely to human experts, highlighting the transformative nature of the technology.

Marking Scheme:

1 mark for explaining "exclusive domain" as tasks done only by trained professionals.
1 mark for explaining the author's purpose: to highlight the significance of AI's capabilities / the transformative shift.

Common Mistakes:

Students may lift "exclusive domain" directly without paraphrasing. Award 0 for direct lifting.
Students may explain the phrase but fail to address why the author uses it.

Question 3 [2 marks]

Answer: By describing AI's adoption as "rapid," the author conveys that the integration of AI into healthcare has happened much faster than expected. This shapes the reader's understanding by suggesting that the speed of adoption may have outpaced the development of proper ethical guidelines, regulatory frameworks, and safety checks — thereby framing the subsequent discussion of risks and challenges as urgent and necessary.

Marking Scheme:

1 mark for explaining "rapid" as happening faster than expected / at an accelerated pace.
1 mark for linking this to the implication that ethical/regulatory/safety concerns may not have kept pace.

Common Mistakes:

Students may simply define "rapid" as "fast" without contextualising it within the passage's argument about scrutiny and caution.

Question 4 [2 marks]

Answer: The author includes this specific evidence to provide concrete, credible data that supports the claim that AI can match or exceed human performance in certain diagnostic tasks. By citing a reputable source (The Lancet Digital Health) and specific statistics (94.5% vs. 88.0% sensitivity), the author strengthens the argument that AI has genuine potential in healthcare, making the subsequent discussion of its limitations more balanced and persuasive.

Marking Scheme:

1 mark for identifying the purpose: to support the claim that AI can rival or surpass human radiologists.
1 mark for explaining the effect: it adds credibility / specificity / persuasive force through concrete data and a reputable source.

Common Mistakes:

Students may say "to show AI is better than doctors" without acknowledging the nuance (it supports the argument for AI's potential, not a blanket superiority claim).
Generic answers like "to support the point" without specificity earn only 1 mark.

Question 5 [2 marks]

Answer: The phrase "tempered by a sobering reality" means that the excitement and optimism about AI diagnostics must be moderated or balanced by a serious, realistic consideration of its limitations. "Tempered" suggests that enthusiasm should be restrained, while "sobering reality" refers to the uncomfortable truth that AI systems are limited by the quality of their training data. The author uses this phrase to signal a shift in the passage from discussing AI's promise to examining its problems.

Marking Scheme:

1 mark for explaining "tempered" as moderated / balanced / restrained.
1 mark for explaining "sobering reality" as the serious/uncomfortable truth about AI's limitations (specifically data quality issues).

Common Mistakes:

Students may explain only one part of the phrase. Award 1 mark for partial answers.
Direct lifting of the phrase scores 0.

Question 6(a) [2 marks]

Answer: "Algorithmic bias" refers to the phenomenon where AI systems produce less accurate results for certain groups of people because those groups were underrepresented in the data used to train the algorithms. In the passage, this is illustrated by AI diagnostic tools that performed poorly for patients of African and South Asian descent because these populations were not adequately represented in the training datasets.

Marking Scheme:

1 mark for defining algorithmic bias as AI producing skewed/inaccurate results due to unrepresentative training data.
1 mark for referencing the specific example from the passage (underrepresentation of certain ethnic groups leading to lower accuracy).

Common Mistakes:

Students may give a generic definition of bias without connecting it to AI training data. Award 1 mark only.

Question 6(b) [1 mark]

Answer: The author uses "paradoxically" because algorithmic bias is ironic: AI is intended to improve healthcare access for underserved populations, but due to biased training data, it may actually perform worst for those same populations, thereby widening health disparities instead of narrowing them.

Marking Scheme:

1 mark for identifying the irony/contradiction: AI meant to help underserved groups may end up disadvantaging them.

Common Mistakes:

Students may simply define "paradoxically" as "strangely" without explaining the specific paradox in context. Award 0.

Question 7 [2 marks]

Answer: By describing traditional drug development as "notoriously slow and expensive," the author creates a strong contrast with AI's ability to "dramatically compress this timeline." The word "notoriously" implies that the slowness and cost are widely recognised and accepted as serious problems. This word choice makes AI's contribution seem more impressive and necessary — it positions AI as a solution to a well-known, frustrating problem in the pharmaceutical industry.

Marking Scheme:

1 mark for explaining the effect of "notoriously" — it emphasises that the problem is widely known and significant.
1 mark for explaining how this sets up AI as a solution / creates a contrast that makes AI's role seem more valuable.

Common Mistakes:

Students may only explain "slow and expensive" without addressing the rhetorical effect of "notoriously."

Question 8 [2 marks]

Answer: "A watershed moment" means a critical turning point or a historically significant milestone. In this context, the author uses the phrase to convey that the entry of the first AI-designed drug into Phase III clinical trials represents a landmark achievement that could fundamentally change how drugs are developed in the future. It signals a shift from traditional methods to AI-driven approaches.

Marking Scheme:

1 mark for defining "watershed moment" as a turning point / landmark / pivotal moment.
1 mark for linking it to the significance in context: it marks a fundamental change in drug development.

Common Mistakes:

Students may use the phrase "watershed moment" in their answer without explaining it. Award 0 for direct lifting.

Question 9 [2 marks]

Answer: "Woofully inadequate" means severely or seriously insufficient. The word "woefully" is an intensifier that conveys a sense of regret or dismay, suggesting that the author views the current state of regulatory frameworks as not just inadequate but alarmingly so. This reveals that the author has a concerned, critical attitude toward the lack of proper oversight for AI-assisted drug development, and believes urgent action is needed.

Marking Scheme:

1 mark for explaining "woefully inadequate" as severely/seriously insufficient.
1 mark for identifying the author's attitude: concerned / critical / dismayed, and that the author believes stronger regulation is urgently needed.

Common Mistakes:

Students may explain "inadequate" but miss the intensifying effect of "woefully." Award 1 mark only.

Question 10 [2 marks]

Answer: The author structures this section as a series of questions to engage the reader and emphasise the complexity and unresolved nature of the accountability issue. By posing questions rather than providing answers, the author highlights that there are no easy solutions and that this is a genuine dilemma. The rhetorical questions also force the reader to think critically about who should be held responsible, making the issue feel more urgent and personally relevant.

Marking Scheme:

1 mark for identifying the purpose: to highlight the complexity/unresolved nature of the accountability issue.
1 mark for explaining the effect on the reader: engages the reader / provokes critical thinking / emphasises urgency.

Common Mistakes:

Students may say "to make the reader think" without explaining why questions are more effective than statements in this context. Award 1 mark only for vague answers.

Question 11 [2 marks]

Answer: "The dehumanisation of healthcare" refers to the loss of the personal, empathetic, and human elements of medical care as AI systems take over more clinical decisions. The author explains that medicine is not just a science but also an "art" rooted in the empathetic relationship between doctor and patient. If algorithms replace human judgement, the ability to listen, comfort, and understand patients as whole individuals may be lost, reducing healthcare to a purely technical process.

Marking Scheme:

1 mark for defining dehumanisation as the loss of personal/empathetic elements in healthcare.
1 mark for referencing the doctor-patient relationship / the "art" of medicine / the ability to listen and comfort.

Common Mistakes:

Students may give a generic definition of "dehumanisation" without connecting it to the specific context of healthcare and the doctor-patient relationship.

Question 12 [2 marks]

Answer: "The human touch" refers to the empathetic, personal, and compassionate aspects of medical care that only human doctors can provide — such as listening to patients, offering comfort, and understanding them as whole individuals rather than just sets of symptoms. The author considers this important because medicine is described as "as much an art as a science," meaning that effective healthcare requires emotional connection and human understanding that algorithms cannot replicate. The author fears that over-reliance on AI may diminish this essential quality.

Marking Scheme:

1 mark for explaining "the human touch" as empathy / personal connection / compassion / holistic understanding of patients.
1 mark for explaining why it's important: medicine is an art as well as a science; AI cannot replicate human empathy.

Common Mistakes:

Students may give a vague answer like "being kind to patients" without connecting it to the broader argument about AI vs. human judgement.

Question 13 [2 marks]

Answer: The author includes Dr. Sarah Chen's quotation to provide an expert perspective that encapsulates the passage's central argument about the relationship between AI and human doctors. The quotation serves two purposes: (1) it reassures that AI will not make doctors obsolete, but (2) it warns that doctors who fail to adapt to AI will be left behind. This reinforces the passage's balanced view that AI should complement, not replace, human expertise, and it lends authority to the author's conclusion about responsible implementation.

Marking Scheme:

1 mark for identifying the purpose: to provide an expert opinion / authoritative perspective on AI's role in healthcare.
1 mark for explaining the dual message: AI won't replace doctors, but doctors must adapt; reinforces the idea of AI as a complement, not a replacement.

Common Mistakes:

Students may only explain one half of the quotation's message. Award 1 mark for partial answers.

Question14 [3 marks]

Answer: The author's overall tone is balanced and cautiously critical (or "measured and analytical").

Evidence 1: The author acknowledges the benefits of AI, such as its ability to detect cancer with high accuracy and compress drug development timelines, showing a fair and objective assessment of the technology's potential.

Evidence 2: At the same time, the author consistently raises concerns and limitations, using phrases like "woefully inadequate," "paradoxically," and "legitimate concern," which reveal a critical stance toward the unchecked adoption of AI without proper safeguards.

Marking Scheme:

1 mark for identifying the tone (balanced / cautiously critical / measured / analytical — accept any reasonable description).
1 mark for the first piece of evidence from the passage.
1 mark for the second piece of evidence from the passage.

Common Mistakes:

Students may identify the tone as purely "negative" or "positive." The passage is balanced, so award 0 for one-sided tone descriptions.
Evidence must be specific (quoted phrases or paraphrased ideas), not generic.

Question 15 [4 marks]

Answer: The author argues that responsible implementation is crucial because, while AI holds genuine promise for improving patient outcomes, reducing costs, and expanding access to care, these benefits can only be realised if three conditions are met. First, AI systems must be rigorously tested for bias to ensure they work equitably for all populations. Second, robust regulatory frameworks must be established to govern the safe and ethical use of AI in medicine. Third, AI must be deployed as a complement to human expertise rather than a replacement for it, ensuring that the human element of healthcare is preserved. The author emphasises that the transition must be managed equitably, transparently, and with patient welfare as the top priority.

Marking Scheme:

1 mark for acknowledging AI's promise (improving outcomes, reducing costs, expanding access).
1 mark for mentioning the need to test for bias.
1 mark for mentioning the need for robust regulatory frameworks.
1 mark for mentioning AI as a complement to (not replacement for) human expertise, with patient welfare as paramount.

Common Mistakes:

Students may discuss the entire passage rather than focusing on the final paragraph. Award a maximum of 2 marks if the answer draws from other paragraphs.
Answers that only list points without explanation may lose marks for depth.

Section B: Summary Question [8 marks]

Question 16 [8 marks]

Model Summary (for reference — within 120 words):

The passage highlights several concerns about AI in healthcare. AI systems may exhibit algorithmic bias, performing less accurately for underrepresented ethnic groups due to skewed training data, thereby worsening health disparities. Regulatory frameworks for AI-assisted drug development are insufficient, raising safety concerns. Accountability is unclear when AI errors cause patient harm, as it is uncertain whether hospitals, developers, or clinicians are responsible. Over-reliance on AI risks dehumanising healthcare by diminishing the empathetic doctor-patient relationship. Additionally, the vast amounts of sensitive patient data required for AI training create data privacy vulnerabilities, with healthcare data breaches becoming increasingly common. These challenges must be addressed to ensure AI benefits all patients equitably.

(Word count: 112)

Marking Scheme:

#	Content Point	Marks
1	Algorithmic bias due to unrepresentative/skewed training data	1
2	AI may worsen/widen health disparities (affecting underserved groups most)	1
3	Regulatory frameworks are inadequate/insufficient for AI in drug development	1
4	Accountability is unclear when AI causes harm (who is responsible?)	1
5	Risk of dehumanising healthcare / loss of empathetic doctor-patient relationship	1
6	Data privacy concerns / vulnerability of sensitive patient data	1
7	Healthcare data breaches are increasing	1
8	Language and use of own words (not direct lifting)	1

Total: 8 marks

Marking Notes:

Award content marks (points 1–7) for each valid concern accurately summarised in the student's own words.
Award the language mark (point 8) if the summary is written primarily in the student's own words, in continuous prose, and within the 120-word limit.
Deduct from the language mark if there is significant direct lifting from the passage.
The summary must be written as one continuous paragraph. If written in bullet points or note form, award a maximum of 6/8 (content marks only, no language mark).
Accept any reasonable paraphrasing of the content points.

Section C: Application Question [7 marks]

Question 17(a) [4 marks]

Answer:

Concern 1: Algorithmic bias and unequal accuracy across ethnic groups. The public may worry that the AI diagnostic tools could perform less accurately for certain ethnic groups if the training data underrepresents them. The passage notes that AI tools exhibited lower accuracy for patients of African and South Asian descent due to underrepresentation in training data. Since Singapore is a multiracial society, this is a significant concern — the AI system might not perform equally well for all racial groups, potentially leading to misdiagnosis for some communities.

Concern 2: Data privacy and security of patient information. The public may be concerned about the security of their sensitive health data, even if it is anonymised. The passage highlights that healthcare data breaches are increasingly common, with millions of patient records exposed in cyberattacks. Patients may worry that their health data could be commodified or exploited by third parties, despite government assurances of secure servers.

Marking Scheme:

Marks	Descriptor
2 marks per concern	1 mark for identifying a valid concern + 1 mark for supporting it with evidence from the passage and linking it to the Singapore context.
4 marks	Two well-explained concerns with passage evidence and context.
3 marks	Two concerns but one lacks sufficient evidence or context.
2 marks	One well-explained concern, or two weak concerns.
1 mark	One concern without evidence or context.

Acceptable alternative concerns:

Accountability: Who is responsible if the AI makes an incorrect diagnosis? (Reference to passage lines 36–38.)
Dehumanisation: Patients may feel uncomfortable being diagnosed by a machine rather than a doctor. (Reference to passage lines 40–44.)
Over-reliance on AI: Doctors may become too dependent on AI and lose their clinical judgement. (Reference to passage lines 40–43.)

Common Mistakes:

Students may raise concerns not supported by the passage (e.g., "AI is too expensive"). Award 0 for unsupported concerns.
Students may identify concerns but fail to reference the passage. Award only 1 mark per concern without evidence.

Question 17(b) [3 marks]

Answer (sample — accept any well-justified choice):

Best feature: "All AI recommendations will be reviewed by a qualified doctor before any treatment is prescribed."

This feature best addresses the concerns raised in the passage because it directly tackles two major issues. First, it addresses the accountability problem — since a qualified doctor reviews every AI recommendation, the doctor retains ultimate responsibility for the diagnosis and treatment plan, clarifying the chain of accountability. Second, it addresses the concern about dehumanisation and the loss of the human touch, because patients still interact with and receive care from a human doctor. The AI serves as a tool to assist the doctor rather than replace them, which aligns with the passage's conclusion that AI should complement, not replace, human expertise.

Marking Scheme:

Marks	Descriptor
3 marks	Identifies one feature, provides a clear justification, and links it to specific concerns from the passage.
2 marks	Identifies one feature with some justification, but the link to the passage is weak or underdeveloped.
1 mark	Identifies one feature but provides little or no justification.
0 marks	No valid feature identified, or justification is absent.

Acceptable alternative choices:

AI Ethics Board: Addresses the concern about inadequate regulatory frameworks by establishing a dedicated oversight body to monitor AI deployment and outcomes.
Anonymised data on secure servers: Addresses data privacy concerns by protecting patient information from exploitation and breaches.

Common Mistakes:

Students may choose a feature but fail to justify it with reference to the passage. Award a maximum of 1 mark.
Students may choose more than one feature. Mark only the first one unless instructed otherwise.

— End of Answer Key —

Marks Summary:

Section	Marks
Section A: Questions 1–15	35
Section B: Question 16 (Summary)	8
Section C: Question 17 (Application)	7
Total	50