Punggol residents preparing for 2027 professional learning have a newly identified Singapore Institute of Technology course that asks an important question: how can you tell whether an AI answer is genuinely reliable, rather than merely well written? SIT’s SITLEARN programme lists AI Accuracy, Bias, and Decision Psychology for 7–8 January 2027, 9am–6pm, with in-person classroom teaching at SIT Punggol Campus, 1 Punggol Coast Road, Singapore 828608. The page also lists 4–5 May and 6–7 September 2027. See the university’s original listing.

The booking detail comes first. Although the course’s schedule table publishes those dates, its separate Course Runs panel says “There are no upcoming course runs at the moment.” Consequently 7–8 January is a published planning date, not a confirmed immediately bookable seat. The reviewed page does not give a January application closing date, so this article does not invent one. Ask SITLEARN about actual admission before reserving work leave or paying.

The full course price is S$2,616 including GST. SIT publishes conditional payable examples of S$784.80 for relevant 70%-funding categories and S$304.80 for its stated enhanced-funding category. The training is the second of six courses in the Psychology of AI series, and it can be taken separately. This article explains the dates, course suitability, precision and recall, hallucinations, automation bias and how Punggol families and working adults can evaluate AI outputs responsibly. Information checked 29 October 2026.

Official dates, costs, eligibility and booking status

• Programme: AI Accuracy, Bias, and Decision Psychology
• Provider: Singapore Institute of Technology, SITLEARN
• Scheduled January dates: 7–8 January 2027, 9am–6pm
• Other scheduled 2027 dates: 4–5 May and 6–7 September, 9am–6pm
• Scheduled January 2028: 6–7 January
• Training location: In-person at SIT Punggol Campus, 1 Punggol Coast Road, Singapore 828608
• Booking status: Current Course Runs panel states no upcoming runs
• Exact current application deadline: Not confirmed in the page reviewed
• Full listed fee: S$2,616 including GST
• Eligible 70% funded price shown: S$784.80
• Eligible enhanced-funded price shown: S$304.80
• Learning tool: Capabara AI Factory, in partnership with Straits Interactive
• Place in series: Second of six Psychology of AI courses; may be taken independently
• Entry guidance: Preferably diploma/degree with three years of relevant work experience
• Assessment: Non-credit assignments
• Attainment requirement: 75% attendance and passing the assessment
• Primary source: SITLEARN official course details

The 50-second answer for Punggol residents

This is a two-day professional learning programme scheduled for 7–8 January 2027 at SIT Punggol Campus. The university names the date and fee but does not currently show an available course run for booking. It teaches how to evaluate language-model outputs through accuracy, reliability, bias, hallucination, consistency, precision and recall while recognising psychological traps in human decision-making. The standard fee is S$2,616, with conditional eligible subsidies. Ask SIT for actual registration status and a deadline before making arrangements. It is not an introductory course for primary-school children or a guarantee that any AI system will become error-free.

Why this is more useful than another generic AI talk

Many organisations already know that AI can produce plausible text. The harder question is how to decide when a particular output can support a real decision. This course combines technical evaluation with behavioural psychology, a distinction worth making. A system may perform well on a benchmark while users place too much confidence in its most polished answers. Conversely, an expert may dismiss useful assistance after observing one mistake. The educational goal is calibrated, evidence-based judgement, not enthusiasm or hostility toward AI as an idea.

What the January schedule does and does not confirm

SIT lists 7–8 January 2027, each 9am–6pm, in the published teaching timetable. It also names May and September dates. Those are official scheduled periods. The separate Course Runs section says no upcoming runs, so there is no verified available January seat or application cut-off. A resident can ask for updates, but should not claim a booking has succeeded until SIT confirms it. The difference between a published calendar and an active intake is especially important for employees seeking reimbursement or time away from work.

Why the course is at Punggol, not One Punggol

The provider explicitly states that the course is delivered through in-person sessions at SIT Punggol Campus, 1 Punggol Coast Road. That is the university in Punggol Digital District, not One Punggol Community Club at 1 Punggol Drive or Punggol 21 CC at Punggol Field. Those are separate institutions and locations. Learners should obtain the exact SIT classroom and entry instructions after enrolment. The public course page does not promise a particular room, parking place, shuttle or travel subsidy.

What does AI accuracy mean?

Accuracy generally concerns how often outputs are correct under a defined evaluation. But a single percentage can be misleading if the test questions are unrepresentative or if serious mistakes occur in rare situations. A chatbot might answer many simple factual questions correctly yet fail on an important, specialised one. Before quoting an accuracy figure, ask which tasks were measured, how “correct” was defined, what reference answer was used and when the data was collected. The right test should reflect the intended use rather than a convenient demonstration.

A fictional example of misleading high accuracy

Imagine an AI system evaluating 1,000 routine messages. Suppose 950 are handled correctly, yielding 95% accuracy. That sounds strong, but if the fifty errors disproportionately affect sensitive requests, the error distribution matters. Perhaps the organisation expected manual review of those cases. An overall rate cannot tell a manager whether the system is safe for all functions. These figures are invented solely to demonstrate why subgroup analysis, error severity and the stakes of a decision must accompany a headline percentage.

Precision is a different metric

For a system flagging relevant items, precision measures how many flagged items are actually relevant. If a fictional AI tool flags twenty messages and eighteen genuinely match the intended category, precision is 18 ÷ 20 = 90%. That tells us something about the flags it issued, but not how many relevant messages it missed. A team choosing a tool should decide whether false alarms are costly and whether high precision is more important than finding every relevant case.

Recall answers another question

Recall measures the share of all truly relevant items that the system successfully finds. Suppose there are thirty relevant messages in the same invented example, and the system flags eighteen. Recall would be 18 ÷ 30 = 60%, even though precision is 90%. Both calculations can be correct. A manager reporting only precision might overlook the fact that twelve relevant items were missed. SIT explicitly includes precision and recall because good evaluation requires knowing what each measure can and cannot establish.

Choosing a metric depends on the purpose

A system recommending optional reading material may tolerate a different error balance from one identifying safety-critical incidents. The cost of a false positive, false negative or unsupported recommendation depends on what happens afterward. A model does not become appropriate for every job merely because one benchmark looks strong. The organisation should define the target task, acceptable limitations and human response before choosing which metrics to emphasise. That is operational judgement, not simply a mathematical calculation.

What is an AI hallucination?

A hallucination is an output that presents unsupported or fabricated information as though it were reliable. An example might be a realistic-looking event date, a non-existent citation or a purported rule that the original authority never published. A fluent answer does not prove the source exists. SIT teaches learners to recognise hallucinations and evaluate rates under controlled conditions. In everyday use, verify any material claim against original documents, especially dates, fees, admission conditions, health information and safety requirements.

Why citations must be checked, not merely displayed

AI systems can generate links or references that resemble legitimate evidence. A useful verification habit is to open the cited source and confirm it actually supports the exact statement. A citation to SIT’s general website would not establish that a particular course has a vacant January seat. The actual Course Runs panel is more specific. This is a core reason eduKatePunggol guides identify the original event page and disclose uncertainty rather than present an attractive invented booking link.

What does consistency measure?

A model can produce different answers when the same question is asked under slightly altered wording or on different occasions. Some variation is harmless, while instability in a critical decision may need investigation. SIT’s course covers reliability and consistency across test conditions. A professional evaluator should decide what degree of variation is acceptable for the task and whether inconsistent explanations conceal errors. A single screenshot of a good response does not establish dependable behaviour over time.

Why an answer can look convincing while being wrong

Fluent grammar, numbered sections and precise-sounding numbers can create an impression of authority. Human readers may treat that appearance as evidence rather than checking the calculation or source. SIT’s psychological focus is valuable because the evaluation process can itself be affected by the interface and the evaluator’s expectations. A responsible reader pauses when an answer makes a consequential claim and asks what evidence would actually confirm it. Polished presentation is a communication feature, not a truth test.

Automation bias in a workplace

Automation bias occurs when people rely too heavily on an automated recommendation. Imagine a fictional HR manager accepting an AI summary of a candidate’s qualifications without checking that the system read the correct file. The organisation may then make a poorly supported decision despite having a human in the loop. A meaningful human review requires time, source access and authority to disagree. SIT includes automation bias as part of the course because human oversight can fail even when it formally exists.

Confirmation bias and AI recommendations

Confirmation bias can lead people to notice evidence that supports an existing belief while discounting conflicting information. An AI tool that produces an agreeable recommendation may reinforce that tendency. A useful approach is to ask what evidence would change the decision and to seek disconfirming information from reliable sources. The point is not to force constant disagreement, but to make the reasoning testable. The course’s evaluation framework connects psychological biases with the quality of AI-assisted choices.

Anchoring: the first number sticks

An initial estimate may shape a person’s later judgement even when better information becomes available. Suppose a model suggests a service should cost S$500. A manager might keep adjusting around that figure without establishing how it was produced. A stronger process first defines the cost components and compares independent evidence. Anchoring is one of the psychological distortions SIT’s course identifies. The remedy is not another arbitrary AI estimate; it is careful use of relevant data and independent reasoning.

AI sycophancy and agreeing with the user

A system may favour responses that affirm a user’s opinion instead of correcting unsupported assumptions. This can make interaction pleasant while reducing decision quality. Imagine asking an AI tool whether an unverified school or career plan is certain to work. Excessive agreement could hide risks and limitations. SIT explicitly covers AI sycophancy as a concern in evaluation. A trustworthy tool should sometimes say that a claim is uncertain or contradicted by evidence, particularly where consequences matter.

What the Capabara exercises add

The official course uses Capabara AI Factory in partnership with Straits Interactive. Learners compare model outputs under different conditions and examine systematic errors. That is more instructive than reading a list of common AI mistakes. The page does not promise each participant a permanent product licence or unrestricted access to enterprise records. Use only authorised or synthetic information during exercises; do not upload confidential student, employee or client data without appropriate permission.

Why repeated testing matters

One successful output can be reassuring, but it may be unrepresentative. An evaluation should include a range of appropriate cases, known reference results and a clear process for reviewing failures. Tests need not become arbitrarily complicated; they must reflect the task and consequences. If an organisation deploys a system more widely, it should monitor whether performance changes with different data or software updates. SIT’s course introduces a lightweight assurance audit so professional teams can organise that work.

A basic assurance checklist

A reasonable evaluation begins by identifying the task, expected input and measurable standard of success. Collect representative authorised test examples, record which versions and conditions were used, review failures and identify who can approve deployment. Include an escalation plan for cases beyond the tool’s limits. This is an educational illustration rather than the exact private SIT assessment. Its value is to replace “it seems clever” with documented, repeatable evidence that a system performs a defined function appropriately.

Why model output needs a human owner

When a business acts on an AI-generated recommendation, a responsible person or team should understand who approves the action, which checks are required and what happens when the recommendation is challenged. Merely saying a human is involved is insufficient if the reviewer has no access to evidence or no time to inspect it. An assurance protocol should make responsibility operational. The course is relevant to managers and evaluators who need decisions that remain defensible after the initial excitement about automation has passed.

A school-parent example

Suppose a parent uses AI to compare subject choices for a child entering Secondary school. The answer may offer helpful questions, but the parent must confirm official school-level information, prerequisites and the child’s actual interests. An AI tool cannot replace current MOE instructions or make a guaranteed prediction of a student’s future performance. The useful outcome is improved decision preparation: identify options, verify critical facts, understand uncertainty and discuss choices with the right people.

A small-business example in Punggol

A neighbourhood business may use AI to draft product descriptions or summarise enquiries. Before letting a tool publish pricing or eligibility claims, the owner should test whether it preserves current facts and knows when to ask for human review. One wrong posted price or misleading promise can damage trust. A simple evaluation could use fictional orders and known correct answers. The test should track both accuracy and the cost of mistakes. This illustrates the course’s relevance without suggesting SIT directly manages local businesses.

The first 2027 date and later opportunities

SIT publishes 7–8 January, 4–5 May and 6–7 September 2027. The separate current booking section says no upcoming run, so the later dates must also be treated as planning information rather than guaranteed available alternatives. A person who cannot attend January can ask about the May schedule, but should verify any actual application window when it opens. Do not reuse a deadline from another Psychology of AI course or assume all dates have identical capacity.

What the series structure means

This module is Part 2 of six Psychology of AI courses. SIT says it may be taken independently of the other five. The Psychology of Trust and Human–AI Interaction course is a different module concerned with reliance and trust. This January module places more emphasis on empirical accuracy, model evaluation and bias. An applicant should choose based on their work rather than assume joining the second course automatically includes the whole series.

Who is expected to attend?

SIT names AI project leads, analysts, data-governance staff, compliance and assurance professionals, business decision-makers, educators, researchers and digital transformation specialists. Entry guidance is preferably a degree or diploma with at least three years’ relevant experience. That is a professional audience, not an advertised youth holiday coding camp. Applicants with a different background may ask SIT about suitability. The course does not require every participant to be an AI engineer, but it does expect the ability to analyse decisions and evidence.

Understanding the S$2,616 full fee

The full listed charge is S$2,616 including GST. SIT publishes S$784.80 for the specified eligible 70% funding categories and S$304.80 for its stated enhanced funding category. These are conditional figures. A Punggol address does not establish personal eligibility, and a screenshot showing another learner’s price is not a final quotation. Ask SIT to determine the category and applicable funding before budgeting. Do not assume all credits or subsidies combine automatically.

A fictional two-person training budget

Suppose two eligible employees are independently approved at S$784.80 each. The combined tuition would be S$1,569.60, excluding work coverage or travel. That is arithmetic under an assumption, not a confirmed organisational group discount. The employer should decide what will change after the course—perhaps a better evaluation rubric or documented test set—so the learning has a real operational purpose. Attendance for its own sake is not evidence of improved governance.

The two full workdays

Both published January teaching days are 9am–6pm. A resident with a job or caregiving responsibilities may need to arrange leave and household coverage. Even a local campus visit can involve transport and preparation. The time commitment is distinct from the monetary fee, and an employer may need to assign other staff to cover responsibilities. Since live course-run availability is not confirmed, do not make irreversible arrangements until SIT accepts the application and provides joining details.

Assessment and certificate conditions

SIT lists a non-credit-bearing assignment as the assessment. A Certificate of Attainment requires at least 75% attendance and a pass. Learners who meet attendance but not the assessment requirement may receive a Certificate of Participation according to the published terms. Neither proves that a particular AI system is safe, nor does it create a professional licence to certify every model. The course is training in evaluation and judgement; real deployments still require their own appropriate assessments.

How this differs from the Trust course

The February Psychology of Trust guide examines overtrust, undertrust and how interface design affects reliance. Accuracy and Decision Psychology uses technical evaluation concepts such as precision, recall, consistency and hallucination-rate alongside human bias. There is a meaningful overlap, but one is not a duplicate of the other. A professional can ask whether the immediate problem is testing output reliability or understanding people’s behaviour around those outputs.

Where to go after enrolment

The course is at SIT Punggol Campus, not One Punggol CC. The university address is 1 Punggol Coast Road, Singapore 828608, and SIT’s own location guidance provides current travel context. Confirm the classroom and reporting time through the official joining notice. No particular parking arrangement or personal transport benefit is guaranteed by the fee table.

Frequently asked questions

Dates? 7–8 January 2027, 9am–6pm; May and September alternatives appear in the schedule. Can I book January now? The detailed Course Runs panel currently says no upcoming runs, so direct confirmation is required. Fee? S$2,616 full; conditional eligible S$784.80 or S$304.80. Experience? Preferably diploma/degree and three years’ relevant work. Is coding required? The listing focuses on evaluation and psychology rather than a programming prerequisite. Assessment? Assignments and attendance. Does passing certify any AI vendor? No.

Applicant checklist

Open SITLEARN’s original AI Accuracy course page, compare the published schedule with the current Course Runs notice, and ask when applications actually open. Verify your experience and the course’s intended skill level. Request the correct funding decision, assessment expectations and exact classroom. Consider one real evaluation problem from your work, using only authorised or fictional data. Do not treat a fluent AI demonstration as proof of its reliability, and do not infer a job or salary guarantee from course completion.

Sources and related local reading

Primary official announcement: SIT AI Accuracy, Bias, and Decision Psychology, which identifies the January/May/September dates, funding, independent course status, venue, objectives and no-upcoming-run warning. Related Punggol guides: Punggol Events, Psychology of Trust, and AI literacy in education. Verified 29 October 2026. No live vacancy, registration deadline, individual funding or guaranteed performance outcome is asserted.

Final thought: the best question is what the evidence supports

The scheduled 7–8 January 2027 SIT course addresses a problem that will matter far beyond one AI product: separating persuasive output from proven reliability. It offers a structured professional setting for learning accuracy metrics, bias testing and better human judgement, but the current booking section does not confirm an open intake. Residents should verify admission and funding, then use the course to develop a habit of asking what was tested, what failed and whether a decision is defensible. Useful AI depends on that habit, not merely confident words.