The School of Artificial Intelligence is the division of KBA13 Academy that teaches people how to use AI competently, critically, and responsibly in professional and academic work. Its subject is not the engineering of models. It is the judgment required to apply them, evaluate their output, and remain accountable for the result.
That distinction defines everything the School does. There is no shortage of material explaining what AI tools can produce. What is scarce is instruction in when to trust the output, how to verify it, where it fails in ways that are difficult to detect, and who is answerable when it is wrong. Those are the questions this School exists to answer.
What the School teaches
Teaching is organized around applied competence for people who already have professional expertise and now need to work alongside AI systems. Researchers who must decide whether an AI-assisted literature review is defensible. Lecturers redesigning assessment for a cohort with access to generative tools. Policy staff evaluating an AI-drafted briefing. Managers asked to approve an AI adoption plan they cannot technically assess.
The curriculum therefore runs from conceptual foundations through applied practice to governance. Foundations cover how these systems actually work in outline, why they generate fluent text without verifying it, and what that implies for use. Applied work covers structured prompting, workflow design, verification procedures, and integration into research, teaching, analysis, and policy tasks. Governance covers accountability, disclosure, data handling, institutional policy, and the limits that should be written into any adoption decision.
Programs are grouped into academic programs for structured study, professional programs leading to certificates, and executive programs for leaders responsible for adoption decisions. Individual subjects are listed in the course catalog, and the practical tooling taught across programs is described under AI tools.
The School’s position on AI
The School holds a defined position rather than an enthusiastic one, and it is worth stating plainly because it shapes the teaching.
AI systems are genuinely useful for a specific class of tasks: summarizing long material, translating, restructuring text, generating options to be evaluated, organizing evidence, drafting that a human will rewrite, and accelerating routine work that a competent person could do slowly. Used this way they save substantial time.
AI systems are unreliable for a different class of tasks, and the unreliability is structural rather than a temporary defect. They produce specific, plausible, confidently stated claims that are false. They fabricate citations that look correct in format. They cannot reliably distinguish what they know from what they are generating. No amount of careful prompting removes this, because it follows from how the systems work.
The operating conclusion taught throughout the School is therefore consistent: AI may accelerate work, and never substitutes for human judgment, verification, or accountability. Where AI has contributed materially to a product, the contribution is disclosed. Where an AI-generated factual claim will be relied upon, it is verified against a primary source before use.
Responsible AI as a requirement, not a module
Responsible use is taught inside every subject rather than isolated in one ethics course. A course on AI for research covers verification of AI-suggested sources as part of method. A course on AI for teaching covers assessment integrity as part of design. A course on AI for policy covers disclosure and accountability as part of process.
This structure is deliberate. Governance taught separately is treated as an obligation to be satisfied afterwards. Governance taught inside method is treated as part of doing the work correctly, which is what it is.
Institutional questions of accountability, disclosure, data governance, and human approval are examined most directly in the governance and responsible AI programs. They connect to the wider institutional position set out by the KBA13 AI Laboratory, which handles applied experimentation and development, while this School handles teaching and assessment.
How the School teaches
Every course is knowledge-first. Written explanation, worked examples, exercises using real tasks, and assessed applied work carry the learning. Video, where present, is supplementary.
Assessment is applied. Learners produce work that uses AI on a genuine professional task, then document what the tool contributed, what they verified, what they rejected, and why. Marking weighs the verification and judgment more heavily than the polish of the output, because polished unverified output is the failure mode the School exists to prevent.
What the School is not
It is not a computer-science department and does not train model developers or engineers. It does not teach programming as a primary subject. It does not issue degrees; its certificates are non-degree professional credentials conferring no academic credit, SKS, ECTS, or government accreditation.
It also does not promote AI adoption as an end in itself. A recurring outcome of the executive programs is a decision not to automate a particular function, reached for defensible reasons. Teaching that outcome as legitimate is part of the School’s purpose.
Who studies here
Academics and researchers integrating AI into method. University staff and lecturers addressing teaching, assessment, and institutional policy. Government and policy professionals evaluating AI-assisted analysis. Analysts, including those working through the School of Intelligence & Strategic Studies on AI-assisted analysis. Managers and institutional leaders accountable for adoption decisions. Professionals in any field who use these tools daily and have never been taught to evaluate them.
No prior technical background is required for foundation study. Advanced pathways state prerequisites on each course page.
Common questions about degrees, prerequisites, format, pricing, and recognition are answered on the FAQ page. Institutional cohorts and advisory work are arranged through the proposal request process and KBA13 Consulting.