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Thinka May 2024 SL (TZ2) IB Diploma Programme-Style Mock — Digital society

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An original Thinka practice paper modelled on the structure and difficulty of the May 2024 SL (TZ2) IB Diploma Programme Digital society paper. Not affiliated with or reproduced from IB.

Paper 1

Answer two questions. Each question is worth 20 marks.

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PastPaper.question 1 · Identify

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Identify two protocols used to secure data transmission over the internet.

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PastPaper.workedSolution

1. HTTPS (Hypertext Transfer Protocol Secure): The secure version of HTTP used to encrypt web traffic.
2. SSL/TLS (Secure Sockets Layer / Transport Layer Security): Cryptographic protocols designed to provide communications security over a computer network.

PastPaper.markingScheme

Award [1] mark for each correctly identified secure protocol up to a maximum of [2] marks.
- Acceptable answers include: HTTPS, SSL, TLS, SFTP, SSH, IPsec.
- Do not accept HTTP or FTP without explicit mention of secure versions.

PastPaper.question 2 · Identify

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Identify two types of sensors commonly used by autonomous delivery robots to detect physical obstacles in their immediate path.

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PastPaper.workedSolution

1. LiDAR (Light Detection and Ranging): Uses laser beams to create highly accurate 3D maps of the surrounding environment.
2. Ultrasonic sensors: Emits high-frequency sound waves to detect close-range obstacles, commonly used for collision avoidance.

PastPaper.markingScheme

Award [1] mark for each correctly identified sensor up to a maximum of [2] marks.
- Acceptable answers include: LiDAR, ultrasonic sensors, infrared (IR) sensors, radar, stereoscopic cameras/computer vision cameras, proximity sensors.

PastPaper.question 3 · Identify

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Identify two key characteristics that distinguish machine learning (ML) from traditional rule-based computer programming.

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PastPaper.workedSolution

1. Data-driven learning: Traditional programming relies on human-written, explicit rules ('if-then' statements), whereas machine learning systems identify patterns and generate their own rules from training datasets.
2. Adaptability: Machine learning algorithms can automatically update and improve their accuracy as they process more data, without requiring a programmer to rewrite the code.

PastPaper.markingScheme

Award [1] mark for each distinct valid characteristic identified up to a maximum of [2] marks.
- Examples include: relying on training data instead of hardcoded rules, probabilistic/non-deterministic outputs, dynamic adaptation/self-improvement over time, pattern recognition capabilities.

PastPaper.question 4 · Identify

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Identify two ethical issues associated with the collection and commercialization of user location data by smartphone applications.

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PastPaper.workedSolution

1. Privacy infringement: Apps often collect location data in the background without explicit or fully understood consent, violating users' expectations of confidentiality.
2. Commercial profiling and surveillance: Companies can compile detailed profiles of an individual's habits, beliefs, and relationships based on where they spend time, leading to targeted manipulation or potential leaks to third parties.

PastPaper.markingScheme

Award [1] mark for each clearly identified ethical issue up to a maximum of [2] marks.
- Acceptable answers include: invasion of privacy, lack of genuine or informed consent, surveillance/tracking, unauthorized profiling/commercial exploitation, safety risks (e.g., location leaks resulting in harassment or stalking).

PastPaper.question 5 · Identify

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Identify two economic impacts of the digital platform-based 'gig economy' on independent freelance workers.

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PastPaper.workedSolution

1. Increased flexibility: Workers have greater autonomy to choose their own hours and tasks.
2. Financial instability/lack of benefits: Workers do not receive traditional employment protections, paid leave, sick pay, or health insurance, transferring financial risks from employers to individuals.

PastPaper.markingScheme

Award [1] mark for each distinct economic impact (positive or negative) identified up to a maximum of [2] marks.
- Acceptable answers include: flexibility in work scheduling, access to global/diverse markets, lack of employer-provided benefits (healthcare, pension), income instability/volatility, shifting of operational costs (e.g., vehicle maintenance) to the worker.

PastPaper.question 6 · Identify

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Identify two digital technologies that have significantly enabled the expansion of telemedicine in healthcare.

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PastPaper.workedSolution

1. High-speed video conferencing tools: Allow patients to consult with physicians in real time from home, overcoming geographical barriers.
2. Wearable biosensors and IoT devices: Devices like smartwatches track vital signs (heart rate, blood oxygen) and automatically transmit health data to medical professionals for remote monitoring.

PastPaper.markingScheme

Award [1] mark for each correctly identified digital technology up to a maximum of [2] marks.
- Acceptable answers include: video conferencing/telecommunications software, wearable health trackers/IoT devices, mobile health applications (mHealth apps), cloud-based electronic health records (EHRs), remote patient monitoring systems (RPM).

PastPaper.question 7 · Explain

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Explain how the use of a Virtual Private Network (VPN) secures data transmission for a remote worker accessing a corporate intranet over a public Wi-Fi network.

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PastPaper.workedSolution

1. Encryption: The VPN encrypts the remote worker's internet traffic. Even if a bad actor intercepts the packets on the public Wi-Fi, they only see ciphertext, protecting confidentiality.
2. Tunneling: It wraps the data packets in a secure protocol (such as IPsec or OpenVPN) creating a virtual tunnel directly to the corporate gateway.
3. Authentication & IP Masking: It replaces the worker's public IP with that of the corporate network and ensures only authenticated users establish the connection.
4. Integrity checking: It uses cryptographic checksums to ensure that data packets are not altered during transmission.

PastPaper.markingScheme

Award [1] mark for identifying a security mechanism of a VPN and [1] mark for explaining how it functions to protect remote data transmission, up to [4] marks.
- Encryption: protects confidentiality against eavesdropping (1 mark) + explanation of ciphertext/unreadable data (1 mark).
- Tunneling: establishes a secure point-to-point pathway (1 mark) + explanation of encapsulation (1 mark).
- Authentication/Integrity: prevents unauthorized access or packet tampering (1 mark) + explanation of cryptographic validation (1 mark).

PastPaper.question 8 · Distinguish

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Distinguish between automated systems and autonomous technologies in industrial settings.

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PastPaper.workedSolution

Automated systems are programmed to perform specific, repetitive tasks following fixed inputs (such as a manufacturing robotic arm assembling components in a fixed sequence). They do not adapt to unexpected obstacles. Autonomous technologies, however, possess situational awareness and decision-making capabilities (such as an automated guided vehicle navigating around dynamic obstacles in a warehouse using LiDAR and machine learning) allowing them to alter their behavior based on environmental changes.

PastPaper.markingScheme

Award up to [2] marks for a clear definition and industrial example of automated systems:
- Focuses on predefined rules, repetition, and lack of adaptation (1 mark).
- Providing a relevant industrial example (1 mark).
Award up to [2] marks for a clear definition and industrial example of autonomous technologies:
- Focuses on decision-making, sensor feedback, and adaptation to change (1 mark).
- Providing a relevant industrial example (1 mark).

PastPaper.question 9 · Suggest

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A health-tech company is developing an AI-driven diagnostic app. Suggest two ethical measures the company should implement to ensure digital equity and inclusivity for users from diverse socio-economic backgrounds.

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PastPaper.workedSolution

To ensure digital equity and inclusivity, the company can:
1. Curate highly representative training datasets: This ensures that diagnostic algorithms do not have biased error rates that disadvantage specific demographic groups, ensuring accurate diagnoses for all socio-economic populations.
2. Design for low-bandwidth and offline capabilities: Many individuals from lower socio-economic backgrounds may use older smartphone models or have restricted internet access. Lightweight applications that require minimal data transmission ensure that financial barriers do not prevent access to essential healthcare tools.

PastPaper.markingScheme

Award [1] mark for suggesting a valid ethical measure and [1] mark for explaining how it addresses socio-economic equity/inclusivity, up to [4] marks.
- Measure 1 (Dataset representation): Suggestion (1 mark) + link to avoiding bias/improving equity (1 mark).
- Measure 2 (Accessibility/Low-bandwidth design): Suggestion (1 mark) + link to economic accessibility/affordability (1 mark).

PastPaper.question 10 · extended_response

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Evaluate the decision of a commercial bank to replace human loan officers with an artificial intelligence (AI) automated decision-making system (ADS) to evaluate customer creditworthiness.

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PastPaper.workedSolution

Arguments in favor of ADS implementation:
- Efficiency and scalability: Automated systems can process thousands of applications instantly, lowering overhead costs and speeding up customer service.
- Consistency and reduction of human bias: Algorithmic assessment ensures that every applicant is evaluated using the exact same criteria, potentially removing subjective, personal prejudices of individual human officers.
- Data-driven insight: AI can analyze large, alternative datasets (e.g., utility bills, cash flow patterns) to grant credit access to individuals without traditional credit histories.

Arguments against ADS implementation:
- Algorithmic bias and discrimination: If the training data contains historical biases (e.g., systemic discrimination against specific demographics), the AI will learn and perpetuate these inequalities, locking marginalized groups out of financial services.
- The black box problem: Complex machine learning models lack explainability. It is difficult for the bank to provide clear, legally required justifications to customers regarding why their loans were rejected.
- Lack of contextual empathy: Unlike human loan officers, an AI cannot account for unique qualitative human circumstances (e.g., temporary medical emergencies or highly reliable personal character) that fall outside standard metrics.
- Socioeconomic impacts: Complete replacement leads to immediate job displacement for bank employees and loan officers.

Conclusion/Evaluation:
While an ADS offers significant efficiency gains, the ethical risks of systemic discrimination and lack of explainability are severe. Replacing human officers entirely is premature; a hybrid model where AI supports decision-making while human officers retain final authority and ethical oversight represents a more balanced and responsible approach.

PastPaper.markingScheme

Level of Response Rubric:
- 1-2 marks: Basic identification of some advantages or disadvantages of automated systems, with little development or digital society vocabulary.
- 3-4 marks: Explains some benefits or drawbacks of the system, but the analysis is descriptive, one-sided, or lacks a structured digital society focus.
- 5-6 marks: Provides a balanced discussion of both the positive aspects (efficiency, consistency) and negative aspects (bias, lack of transparency) of using an ADS. Uses relevant terminology but may lack a fully justified conclusion.
- 7-8 marks: Offers a highly structured and critical evaluation. Integrates key concepts (such as justice, power, and ethics). Weighs the trade-offs clearly and concludes with a well-reasoned, justified synthesis (e.g., advocating for a human-in-the-loop approach).

PastPaper.question 11 · extended_response

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Discuss the social and ethical implications of implementing autonomous robotic assistants to provide physical care and companionship in residential homes for the elderly.

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PastPaper.workedSolution

Social Implications:
- Staff relief and support: Robots can take over repetitive, physically demanding tasks (e.g., heavy lifting, delivering meals), reducing caregiver burnout and allowing human staff to focus on high-quality care.
- Social isolation and relationships: Although designed for companionship, excessive reliance on robots may decrease actual human interaction, potentially exacerbating loneliness if families and staff assume the robot fulfills all social needs.

Ethical Implications:
- Human dignity and objectification: Receiving intimate care (e.g., bathing or feeding) from a machine can feel depersonalizing and strip elderly individuals of their human dignity, treating them as biological units rather than persons.
- Deception and psychological impact: Using humanoid robots that mimic emotional responses can deceive vulnerable individuals (such as those with dementia) into believing the robot has genuine feelings, raising concerns about emotional manipulation.
- Privacy and data security: Autonomous navigation requires constant sensor and camera surveillance. This continuous collection of private data in personal living spaces poses significant privacy challenges and security risks.

Conclusion/Synthesis:
While autonomous robots provide vital support for physical care and administrative efficiency, their introduction must be carefully managed to ensure they complement rather than substitute human care. Human dignity and genuine social connection must remain the central priorities in elder care.

PastPaper.markingScheme

Level of Response Rubric:
- 1-2 marks: Simple points listing what robots can do in care homes, with minimal reference to social or ethical concepts.
- 3-4 marks: Explains either social or ethical implications in some detail, but the answer lacks balance or fails to address both dimensions adequately.
- 5-6 marks: Balanced discussion addressing both social (e.g., isolation, staff relief) and ethical (e.g., dignity, privacy, deception) implications. Good use of appropriate digital society terminology.
- 7-8 marks: Excellent, comprehensive discussion that clearly distinguishes and connects social and ethical impacts. Demonstrates a nuanced understanding of the complexities of autonomous technologies in healthcare and ends with a cohesive, well-reasoned conclusion.

Paper 2

Refer to the source booklet. Answer all questions.

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PastPaper.question 1 · Identify

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Refer to the source booklet. Figure 1 shows a fleet of autonomous delivery robots operating on university campuses to transport food orders. Identify two sensors that an autonomous delivery robot uses to detect and avoid obstacles in its path.

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PastPaper.workedSolution

Autonomous delivery robots rely on a suite of onboard sensors to perceive their environment and safely navigate around obstacles. 1. LiDAR (Light Detection and Ranging): Emits laser pulses to measure distances and construct a detailed 3D map of the surroundings. 2. Ultrasonic sensors: Use sound waves to detect the proximity of nearby objects, particularly useful for close-range detection.

PastPaper.markingScheme

Award [1] for each correct sensor identified, up to [2]. Answers may include: LiDAR / Laser scanners, Ultrasonic sensors, Cameras / Computer vision / Image sensors, Radar, Infrared (IR) sensors. Note: Do not accept GPS, as GPS is used for global positioning and path planning rather than direct obstacle detection.

PastPaper.question 2 · Suggest

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A municipality is planning to deploy an AI-driven predictive policing system to allocate police patrols in different neighborhoods based on historical crime data. Suggest two distinct measures that the municipality could implement to mitigate the risk of algorithmic bias in this system.

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PastPaper.workedSolution

1. **Auditing and pre-processing historical training data**: The municipality can analyze historical arrest and crime data for systemic biases (e.g., disproportionate targeting of minority neighborhoods) and apply pre-processing techniques, such as re-weighing or removing biased proxy variables (like postal codes), before feeding it to the AI system. This prevents the algorithm from reinforcing and amplifying past discriminatory policing patterns.

2. **Implementing regular external audits and human-in-the-loop oversight**: The municipality can establish an independent, multi-stakeholder oversight board consisting of data scientists, community leaders, and legal experts. This board would conduct periodic audits of the model's outputs to check for disparate impacts across different demographic groups and ensure that final deployment decisions are made by human officers rather than the system acting autonomously.

PastPaper.markingScheme

Award [1] for identifying a valid measure and [1] for an explanation/development of how it mitigates algorithmic bias in the context of the predictive policing system, up to a maximum of [2] per suggestion.

**Suggested measures may include:**
- **Data auditing / cleaning:** Auditing training data to identify and correct historical biases or disproportionate reporting (e.g., over-policing certain neighborhoods in the past). [1] This ensures the AI does not learn and replicate discriminatory historical patterns. [1]
- **Feature selection / removing proxy variables:** Eliminating sensitive attributes or proxies (such as ZIP/postcodes or race) from the dataset. [1] This reduces the risk of the model indirectly discriminating against marginalized groups. [1]
- **Human-in-the-loop oversight:** Requiring human officers/administrators to review and approve patrol recommendations rather than automating deployment. [1] This allows human judgment to override biased algorithmic outputs. [1]
- **Regular independent performance audits:** Hiring external third-party experts to evaluate the model's fairness and accuracy across different demographics over time. [1] This identifies 'drift' or newly emerging biases in real-world deployment. [1]
- **Community feedback loops / transparency:** Creating open channels for public feedback and publishing algorithmic impact assessments. [1] This ensures public accountability and helps surface community concerns regarding biased outcomes. [1]

PastPaper.question 3 · Compare and contrast

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Refer to the following scenario: A logistics company, FastPack, is updating its fulfillment centers. They are evaluating two pathways: implementing collaborative robots (cobots) that work alongside human workers to sort packages, or deploying a fully autonomous robotic sorting system that operates in restricted, human-free zones. Compare and contrast the implementation of collaborative robots (cobots) and fully autonomous robotic sorting systems in fulfillment centers like FastPack.

PastPaper.showAnswers

PastPaper.workedSolution

Similarities:

1. Technical Foundation: Both technologies rely on advanced sensors (such as LiDAR, computer vision, and infrared) and control algorithms to navigate, identify packages, and make sorting decisions, thereby improving efficiency and reducing sorting errors compared to purely manual human labor.

2. Economic Investment: Both options require significant initial capital expenditure (CapEx) for hardware, software licensing, and integration, along with ongoing maintenance costs, shifting the organization's financial model from variable labor costs to fixed technical assets.

Differences:

1. Safety and Physical Layout: Cobots are designed with force-limiting sensors, soft materials, and rounded edges, allowing them to safely share the same physical workspace with human employees without protective barriers. In contrast, fully autonomous robots operate at higher speeds and with greater force, requiring dedicated, restricted human-free zones (such as safety cages, geofenced areas, or light curtains) to prevent severe physical injuries.

2. Impact on the Workforce: Cobots assist and augment human workers, keeping them directly involved in the process while requiring them to upskill (e.g., learning to program, guide, or troubleshoot the cobot), which can lead to job enrichment. Fully autonomous systems aim to completely automate the sorting task, leading to direct displacement of low-skilled warehouse workers while creating a smaller number of high-skilled monitoring and maintenance roles.

PastPaper.markingScheme

For a maximum of 6 marks, the response must provide a balanced comparison (similarities) and contrast (differences) between cobots and fully autonomous systems.

[1-2 marks]: The response is mainly descriptive, identifying basic characteristics of one or both systems with little or no effective comparison or contrast.

[3-4 marks]: The response offers some structured comparison and contrast, identifying at least one similarity and one difference, but may lack depth in explaining the technical, economic, or safety implications for FastPack.

[5-6 marks]: The response provides a well-balanced, detailed, and highly structured compare and contrast analysis. It includes at least one clear similarity and two clear differences (or vice versa) with explicit reference to digital society concepts such as safety protocols, workflow integration, economic costs, or workforce impacts within the FastPack context.

PastPaper.question 4 · Compare and contrast

6 PastPaper.marks

PastPaper.showAnswers

PastPaper.workedSolution

Similarities:

Differences:

PastPaper.markingScheme

For a maximum of 6 marks, the response must provide a balanced comparison (similarities) and contrast (differences) between cobots and fully autonomous systems.

[1-2 marks]: The response is mainly descriptive, identifying basic characteristics of one or both systems with little or no effective comparison or contrast.

PastPaper.question 5 · Discuss

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Refer to the source context below.

**Context: The Nova City Autonomous Drone Initiative**
Nova City has recently authorized the pilot deployment of 'SkyParcel', a network of fully autonomous delivery drones operating within metropolitan airspace. SkyParcel utilizing advanced AI, computer vision, and LIDAR sensors to navigate congested urban areas without human intervention. The local government supports this initiative to lower inner-city carbon emissions and ease road congestion. However, local delivery driver unions have protested against impending job displacement. Concurrently, citizens' advocacy groups have raised alarms about the continuous video and data capture from drone-mounted navigation sensors, alongside concerns over persistent acoustic noise in residential areas.

**Question:**
Discuss the ethical, social, and economic implications of deploying fully autonomous drone delivery networks in metropolitan areas.

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PastPaper.workedSolution

### Suggested Response Structure

**Introduction**
* Introduce the core technology: fully autonomous drones utilizing AI, LIDAR, and computer vision.
* Define the scope of the discussion, focusing on the trade-offs between economic efficiency/environmental sustainability and socio-ethical concerns (privacy, labor market disruptions).

**Economic Implications**
* *Positive:* Reduced operational costs for logistics companies (lower wages, cheaper energy costs compared to fossil-fuel vehicles). Increased delivery speed and efficiency ('last-mile' optimization).
* *Negative:* Structural unemployment and labor displacement for courier, gig-economy, and truck drivers. Economic transition costs for local municipalities dealing with retraining displaced workforces.

**Social Implications**
* *Positive:* Reduced ground-level traffic congestion, leading to safer roads and fewer pedestrian accidents. Environmental/health benefits from decreased greenhouse gas emissions in urban centers.
* *Negative:* Acoustic pollution (continuous buzzing of drone rotors) affecting mental health and quality of life in dense neighborhoods. Public resistance and unequal distribution of benefits (e.g., wealthier neighborhoods getting faster deliveries while poorer areas experience more visual and noise disturbance).

**Ethical Implications**
* *Privacy:* Continuous surveillance threat. Drones require computer vision cameras and sensors for obstacle avoidance, which may capture high-definition footage of private properties, backyards, and individuals without consent.
* *Data Governance:* Questions around who owns, stores, and accesses the navigational and video data collected by these drone fleets (e.g., private operators vs. state surveillance).
* *Safety and Liability:* Ethical responsibility in the event of hardware failure, battery ignition, or collision with wildlife or bystanders. If an autonomous drone crashes, is liability held by the software developer, the manufacturer, or the logistics company?

**Conclusion**
* A balanced synthesis summarizing that while the economic and environmental promises of autonomous drone networks are substantial, they must be mediated by robust regulatory frameworks (such as restricted flight paths, strict data-purging policies, and proactive labor-transition schemes) to mitigate severe social and ethical drawbacks.

PastPaper.markingScheme

**Markband Descriptors (12 Marks Total)**

* **9–12 marks:**
* The response demonstrates a highly detailed and accurate understanding of autonomous drone systems (sensors, AI, navigation) and their integration with social, ethical, and economic contexts.
* There is a well-structured, balanced, and critical discussion that fully addresses all three dimensions (ethical, social, and economic).
* Arguments are supported by highly relevant examples and clear digital society terminology.
* Evaluative conclusions are insightful, well-justified, and directly address the trade-offs of the technology.

* **6–8 marks:**
* The response demonstrates good understanding of autonomous drone systems and relates them to social, ethical, or economic implications.
* The discussion is mostly balanced, though it may focus more heavily on one or two dimensions (e.g., economic and ethical, while neglecting social).
* Relevant terminology is used, and some attempt at evaluation/synthesis is present.

* **3–5 marks:**
* The response shows basic knowledge of drone technology and identifies some implications.
* The response is largely descriptive rather than analytical, with limited discussion of deeper ethical or social complexities.
* Terminology may be missing or used inappropriately.

* **1–2 marks:**
* The response is minimal, containing only vague or superficial points about drones.
* Little to no analytical or evaluative structure is present.

* **0 marks:**
* No rewardable content.

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