{"resourceId":"abc-unsw-kingstonia-simulated-election-ai-bot-competition-students-20261031","versions":[{"version":"external-f8e289e4819a27406eb426966bd1ed2e20fc4a83bc2b8cceb497c5c596db96dd","resource":{"id":"abc-unsw-kingstonia-simulated-election-ai-bot-competition-students-20261031","title":"UNSW students build AI bots to sway a simulated election in a month-long competition meant to teach them to spot AI manipulation","organization":"University of New South Wales; reported by ABC News (Australia)","sector":"Higher education computing teaching and AI literacy","geography":"Australia (Sydney)","publishedAt":"ABC News, 31 October 2026","publicationDate":"2026-10-31","eventDate":null,"sourceName":"ABC News (Australia)","sourceLabel":"Public broadcaster news feature","sourceUrl":"https://www.abc.net.au/news/2026-10-31/unsw-students-fake-election-ai-bots/107326784","evidenceClass":"independent-reporting","outcomeClass":"emerging","topics":["knowledge-work","developers-agents"],"finding":"ABC News reported on 31 October 2026 that UNSW researchers run a month-long competition in which student teams build AI bots to post, like, share and reply on a purpose-built simulated platform, Legit Social, to sway AI-simulated citizens in a fictional referendum. Teams score for attention, changing views and correctly flagging rival bots. A UNSW senior lecturer says the aim is for students to learn attacker and defender techniques in a safe environment, and that researchers use the game to study AI interference. A preliminary paper on last year's competition found some participants turned to high-volume posting because the scoring rewarded engagement. A student moderator said it helped her recognise bot-like posts. No learning outcome data are reported.","sledRelevance":"An example of experiential AI literacy teaching in higher education, with an early design lesson: scoring rules shape student behaviour. Limited relevance beyond computing courses.","evidence":"News feature with lecturer and student interviews and reference to a preliminary paper; no evaluation.","architectureImplications":"Interpretation: a sandboxed simulation platform lets students build agents without touching real platforms.","governanceImplications":"Interpretation: offensive-technique teaching needs a contained environment and clear rules, as UNSW describes.","securityPrivacyImplications":"The sandbox avoids real users and real platforms; no other data issues reported.","caveats":"Self-reported benefit from one student; preliminary paper not reviewed; election context dominates the story.","streamIds":["student-success"],"roles":{"sales":"Sales framing: UNSW offers a vivid example of hands-on AI literacy: students build bots inside a sandbox to learn how manipulation works and how to catch it. For computing and media programs, this supports a conversation about experiential AI courses with clear safety rules. A bounded offer is help designing a contained simulation exercise and its assessment. Do not claim it improves students' ability to detect misinformation; the article reports no measured outcomes and the relevance outside computing is limited.","engineering":"Engineering framing: the design is a purpose-built simulated social platform with AI-driven citizens and student-built agents that post and reply through it. Fit depends on hosting a contained environment with rate limits, logging and scoring. Prerequisites include model access for simulated users and student agents and a cost budget. Proof of value should test whether scoring rules reward the intended skills, since last year's competitors gamed engagement with high-volume posting, and measure bot-detection accuracy before and after.","delivery":"Delivery framing: the owner is the teaching school running the course, with research ethics oversight because the platform doubles as a study environment. Work includes setting rules of engagement, designing scoring that rewards detection and quality over volume, and assessing learning. Acceptance should rest on measured detection skill and no activity outside the sandbox. Risks include students practising manipulation without reflection, scoring that rewards spam, and research consent issues for participating students."},"roleDetails":{"sales":{"customerProblem":"Programs want students to understand AI manipulation and agent behaviour, but lecture-based AI literacy is abstract.","stakeholders":["Head of computing or media school","Course convenors","Research ethics office","Teaching and learning centre"],"discoveryQuestions":["Do any courses teach AI threats hands-on?","Is there a safe environment for students to build agents?","How would you assess detection skills?"],"valueHypothesis":"Interpretation: a contained simulation can give students practical experience of AI manipulation and detection.","potentialEngagement":"Design of one sandboxed simulation exercise with scoring and a pre and post skills assessment.","unsupportedClaims":["Improves misinformation detection","Transfers to non-computing students","Reduces real-world manipulation"]},"engineering":{"fit":"Sandboxed multi-agent simulation for teaching and research.","architectureIntegration":"Purpose-built platform hosting simulated citizens and student agents, with logging, scoring and moderation.","prerequisites":["Model access and budget","Contained hosting","Logging and scoring engine"],"constraints":["Model costs over a month-long run","Scoring can be gamed","No outcome data"],"security":"Keep agents confined to the sandbox; no connection to real platforms or users.","proofOfValue":["Bot-detection accuracy before and after","Check that scoring does not reward high-volume posting","Zero activity outside the sandbox"]},"delivery":{"work":"Run a contained AI agent simulation exercise with rules, scoring and assessment of learning.","dependencies":["Platform maintenance","Ethics approval where data are studied","Instructor time for moderation"],"ownership":"Teaching school course convenors, with research ethics oversight.","skillsAdoption":"Students need briefing on rules and reflection on the ethics of the techniques they practise.","governanceCheckpoints":["Rules of engagement approved","Ethics review for research use","Post-competition review of scoring"],"acceptanceCriteria":["Proposed: measured gain in bot-detection accuracy","Proposed: no out-of-sandbox activity","Proposed: scoring revised if volume posting dominates"],"risks":["Practising manipulation without reflection","Gamed scoring","Consent issues for research use"]}},"retrievedAt":"2026-11-02T14:34:29Z","enrichedAt":"2026-11-02T14:38:10Z","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Interpretation: builds skills relevant to trust and safety and cybersecurity work; not measured.","procurementImplications":"Limited relevance.","operatingModelImplications":"Interpretation: teaching and research share the same platform, which needs maintenance and ethics oversight.","peerReview":"unknown","sourceVerification":{"openedUrl":"https://www.abc.net.au/news/2026-10-31/unsw-students-fake-election-ai-bots/107326784","referenceExcerpt":"It found some participants turned to high-volume posting because the scoring rewarded engagement.","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}