What makes an IB Digital Society IA topic strong?

A strong topic identifies one specific digital system, a defined context, and people or communities who experience its effects. It creates room to compare claims and perspectives rather than leading to a simple factual answer. You should be able to connect it naturally to course concepts, content, and contexts without forcing every syllabus term into the project. It must also have enough credible evidence and a realistic opportunity for ethical first-hand research. Most importantly, the topic should allow you to evaluate trade-offs and reach a qualified, evidence-based conclusion.

Can I investigate artificial intelligence for my Digital Society IA?

Yes, but artificial intelligence alone is much too broad for an inquiry focus. Select one AI system or practice, such as an automated hiring score, school chatbot, facial-recognition deployment, recommendation system, or deepfake reporting tool. Then define the affected group, location, and impact you intend to evaluate. Your project should emphasize consequences for people and communities rather than providing a technical history of AI. A focused AI case usually works well because it raises questions about accountability, bias, privacy, power, and transparency.

Does a Digital Society IA topic have to be recent?

The case should be relevant to contemporary digital society, but the IB does not require you to select a story from the current week. A slightly older system with stable documentation may be more useful than a new controversy supported only by speculative reporting. Recent regulations and platform changes are valuable when they create clear before-and-after comparisons or competing stakeholder claims. Define the period covered by your inquiry so that your evidence has a consistent boundary. If the system changes during your investigation, acknowledge that development as a limitation or emerging trend.

What type of primary research should I use for the Digital Society IA?

Suitable first-hand research may include interviews, questionnaires, observations, content analysis, or a carefully structured comparison of a digital system’s outputs. The method should answer part of your inquiry rather than being included merely to demonstrate effort. Interviews are particularly useful for understanding stakeholder reasoning, while questionnaires can reveal patterns in awareness, experience, or trust. Do not treat a small convenience sample as proof of how an entire population behaves. Protect participants through informed consent, data minimization, anonymity where appropriate, and compliance with your school’s research procedures.

Can I base my IA on TikTok, Instagram, or another social media platform?

Yes, but the platform name must be narrowed to a particular feature, policy, or digital process. Possible systems include a recommendation feed, age-assurance process, content-reporting workflow, political-advertising library, or visibility-ranking mechanism. Define a user group and context, such as first-time voters in one country or students affected by a specific policy. Avoid attempting to prove that an entire platform causes addiction, polarization, or poor mental health because those claims require evidence beyond the scope of most IAs. A better inquiry evaluates how a named feature influences a particular behaviour, opportunity, risk, or distribution of power.

Should I choose an original topic that nobody else has studied?

Originality does not require discovering a technology that no other student has considered. A familiar subject can produce a strong IA when the system, context, stakeholders, and evidence are precisely defined. In fact, a completely unexplored case may leave you without credible sources or informed perspectives. Your individual contribution comes from how you frame the inquiry, conduct first-hand research, evaluate evidence, and justify your conclusion. Choose a topic that is distinctive enough to sustain your interest but documented well enough to support rigorous analysis.

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IB Digital Society IA Topics That Actually Work

IB Digital Society Internal Assessment IB IA

• 28 Sept 2026•12

IB Digital Society IA Topics That Actually Work

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IB Digital Society IA Topics That Actually Work | RevisionDojo

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IB Digital Society IA Topics That Actually Work

IB Digital Society Internal Assessment IB IA

• 28 Sept 2026•12

IB Digital Society IA topics work best when they focus on one identifiable digital system, a defined group of people, and an impact that can be evaluated from competing perspectives. Broad themes such as artificial intelligence, privacy, and social media are not sufficient by themselves.

Strong topics also provide access to credible sources and realistic first-hand research. This guide explains the official task, suggests current case studies, and shows how to turn an interesting digital issue into a manageable inquiry question.

What the Digital Society IA requires

The official IB term for the internal assessment is the inquiry project. According to the IB Digital Society subject brief, students investigate the impacts and implications of a chosen digital system for people and communities. The requirements are common to SL and HL, although the project contributes 30% at SL and 20% at HL.

The completed project consists of:

An inquiry process document with a maximum of 1,500 words
A recorded multimedia presentation lasting no more than 10 minutes
A complete list of references

The inquiry process document establishes the focus and examines claims and perspectives from three selected sources. The presentation develops the analysis, evaluation, conclusion, emerging trends, and future developments. The IB Digital Society curriculum update also describes the assignment as a student-led inquiry involving first-hand research.

A suitable topic must therefore support more than a report about how a technology works. It needs consequences for people, competing claims, evidence that can be evaluated, and an opportunity for ethical primary research.

The five-part test for a workable topic

Before committing to an idea, apply these five checks:

Can you name the system? Identify the platform feature, algorithm, database, biometric tool, or automated process rather than a general technology.
Can you define the context? Specify a country, city, school, workplace, platform, or affected community.
Are stakeholders in tension? Strong topics involve groups with different interests, such as users, companies, regulators, workers, parents, or schools.
Can you find three substantial sources? Prioritize research, regulation, technical evaluation, company documentation, and informed criticism over repeated news summaries.
Can you conduct first-hand research safely? Interviews, questionnaires, observations, or structured comparisons must be feasible, relevant, and ethical.

The RevisionDojo topic-selection guide summarizes the essential combination as a focused system, named stakeholders, ethical tension, relevant concepts, and manageable scope.

Current IB Digital Society IA topics

These questions are models rather than titles to copy. Adapt the population, location, time frame, and system according to the evidence and participants you can genuinely access.

Case study	Possible inquiry question	First-hand research option
Australian social media age assurance	To what extent do age-assurance systems protect Australians under 16 without disproportionately reducing privacy and access?	Interview parents or educators; survey attitudes without collecting identity data
Automated hiring in New York City	To what extent do mandatory bias audits make automated hiring tools accountable to applicants?	Interview recruiters or applicants; compare published audit notices
EU facial-recognition restrictions	To what extent do restrictions on real-time biometric identification balance public safety and civil liberties?	Interview a legal or technology specialist; survey public perceptions
Surveillance pricing	How does data-driven personalized pricing affect consumer autonomy and fairness in US online retail?	Conduct controlled price comparisons; interview consumers about disclosure
Deepfakes in schools	How effectively can school reporting and media-literacy systems reduce harm from AI-generated deepfakes?	Interview safeguarding staff; use non-explicit hypothetical scenarios
Generative AI in education	To what extent do school AI assistants redistribute control over student data and learning?	Interview teachers; survey students about trust and use
Short-video recommendations	How does a named recommendation feed influence political information exposure among first-time voters?	Use interviews or a limited content diary with consent
App-based delivery work	To what extent do task-allocation and rating systems affect delivery workers’ autonomy in a named city?	Interview adult workers without collecting identifying employment data
Wearable health monitoring	How does a smartwatch health-alert system influence users’ understanding of responsibility for their health?	Interview adult users; compare interpretations of published warnings
School behaviour platforms	To what extent does a named behaviour platform improve consistency while increasing student surveillance?	Analyse policies and interview participants with school approval

Why these case studies are productive

Australia’s age-assurance policy creates a particularly clear case. From 10 December 2025, covered platforms have been required to take reasonable steps to prevent Australians under 16 from creating or keeping accounts, according to the Australian eSafety Commissioner. A focused investigation could examine age estimation, identity verification, privacy, accuracy, access, or platform responsibility.

New York City’s Local Law 144 provides a similarly bounded example of algorithmic governance. Covered employers generally cannot use an automated employment decision tool unless it has received a recent bias audit, results are publicly available, and candidates receive required notices. The New York City guidance allows students to evaluate whether audits and disclosure meaningfully redistribute power to applicants.

Facial recognition supports analysis of public safety, surveillance, consent, discrimination, and state power. The EU AI Act restricts real-time remote biometric identification in public spaces for law-enforcement purposes, subject to limited exceptions and safeguards described in the European Commission’s explanation. Technical evidence also matters because the NIST demographic evaluation shows that error rates can vary across algorithms and demographic groups.

Surveillance pricing connects personal data with commercial power. In 2025, the US Federal Trade Commission reported that information such as location, browser history, shopping behaviour, and mouse movements could inform tools used to target prices or promotions. The FTC surveillance pricing study should be interpreted cautiously because it presents initial findings rather than proof that every examined company acted unlawfully.

Deepfakes and educational AI can also work, but they require careful ethical boundaries. The European Parliament briefing on children and deepfakes discusses deception, cyberbullying, exploitation, and difficulties identifying synthetic media. The UK Information Commissioner’s guidance on children’s data and educational technology provides a useful basis for examining data minimization, purpose, control, and accountability.

Turning a broad idea into an inquiry question

A practical structure is:

To what extent does [specific digital system] influence [defined impact] for [named group] in [bounded context]?

For example, “social media and teenagers” is too broad. A stronger version is: “To what extent does age estimation used by social media platforms protect Australians aged 13-15 while preserving their privacy?”

Check that the wording:

Requires a reasoned judgment rather than description
Keeps the digital system central
Identifies affected people or communities
Allows benefits, harms, and uncertainties to be considered
Can be answered using accessible evidence

The RevisionDojo research-question guide offers additional structures. Treat them as scaffolds and revise the language to reflect your own case and evidence.

Common topic-selection mistakes

A theme such as “AI ethics” is not a specific system. Replace it with a ranking feed, age-estimation model, hiring score, chatbot, or biometric matching process operating in a defined context.

Avoid relying on one perspective. Company material may explain intended benefits, but your evidence should also include regulators, independent researchers, affected groups, civil-society organizations, or technical evaluators.

Do not treat primary research as a popularity poll. A small questionnaire can reveal experiences, awareness, or trust, but it cannot establish whether a system is accurate, lawful, or effective for an entire population.

Research ethics also affect feasibility. Avoid collecting unnecessary personal data, asking for traumatic disclosures, using deceptive experiments, or involving vulnerable participants without suitable safeguards. For sensitive topics, analyse policies or interview responsible adults instead.

A practical selection workflow

Shortlist three ideas and record the system, stakeholders, context, relevant concepts, three promising sources, primary research method, and ethical concerns for each. Then read enough evidence to confirm that genuine disagreement exists.

Use the RevisionDojo Digital Society IA guide to review the assessment structure and inspect annotated Digital Society IA examples for models of focus and argument. Examples should clarify expectations, not provide wording to copy.

Finally, explain your inquiry in two sentences. If this requires a page, the scope is probably too broad. If the question produces an obvious yes or no answer, it needs greater tension.

Conclusion

Effective IB Digital Society IA topics combine one specific digital system, a bounded context, identifiable stakeholders, competing perspectives, credible evidence, and ethical first-hand research. Cases such as age assurance, automated hiring, facial recognition, surveillance pricing, deepfakes, and educational AI work because they create genuine tensions involving rights, benefits, power, and responsibility.

Choose a case for its research potential rather than its popularity. RevisionDojo’s Digital Society IA Guide, coursework exemplars, Jojo AI, and IA Feedback can help you test your focus and develop an evidence-based argument.