A self-paced, tool-neutral course for people who design, deliver, commission or measure workplace learning. You follow one L&D manager, Ren, through 24 weeks and one stubborn performance problem, and you make the decisions alongside: what the real need is, what an AI draft got wrong, which number is learning and which is not. Imagine walking into your next sponsor meeting with one sentence that states the gap, the measure and the share of it that training can fix. About 11 hours over 56 days.
Bodhih Applied AI: Learning and Development is a self-paced online course for L&D managers, instructional designers, trainers and HR partners. Eight 75-minute modules follow one learning team through a real performance problem and cover needs analysis, evidence-based design, checking AI-drafted content and questions, AI coaches and role-plays, personalisation, evaluation and responsible use. Each module has an animated video, a browser-based decision simulation and a task on your own work. Learners take a baseline and a final assessment and earn a Bodhih credential with an Open Badge.
Facts on this page last checked by Siddharthan VGJ, Director, Bodhih Training.
The iceberg under the request. The animated concept video from Module 1 (The course everyone finished: finding the real need), narrated by a synthetic voice, with captions.
You suspect that the problem is not a training problem. The request now arrives with a deadline that assumes a tool will do the work. A faster route to the wrong course helps nobody, and you need a way to say so that a sponsor will accept.
An AI tool gives you forty tidy pages or sixty tidy questions in an afternoon. The errors sit in confident sentences that read exactly like the correct ones. Reading it through does not find them, and your subject expert has one hour.
Vendors promise personalised, AI-powered learning, and practice scores go up when the coach is switched on. None of that tells you what people can do on the job without the tool. You need a test for the claims, and a number you can defend.
For people who decide what gets built, for whom, and how it is judged. You practise telling a sponsor what training can and cannot fix, and reporting a result without overclaiming.
For people who turn a need into a design, content and assessments. You practise briefing an AI tool, auditing its draft against learning science, and running a claim check and a question review.
For people who deliver sessions, coach others or are asked to teach what they know. You practise setting rules for AI practice so that learners think first, and judging it by what they can do alone.
For people who bring the business problem, or who run the platform and its data. Picture your next vendor demo with two questions ready: what is the score for, and who sees it?
This course is not for you if you want a first course in using AI at work (see Bodhih AI at Work), a tutorial for one authoring tool, a course in building AI agents, or legal advice: Modules 6 and 8 teach principles and the questions to take to your legal and data protection leads.
Five dated findings, each linked to its source, read at the original on 14 November 2026 and stated with its limits. Most come from schools, universities and medical education, not from corporate training, and the course says so.
In a field experiment with nearly a thousand students, an ordinary AI chat tool lifted practice scores, but once it was taken away those students did worse than classmates who never had it. A version designed to give hints rather than answers largely avoided the harm. School mathematics, in one country.
Bastani and colleagues, “Generative AI without guardrails can harm learning: Evidence from high school mathematics”, Proceedings of the National Academy of Sciences 122(26), 2025; read on 14 November 2026In one study of AI-written practice questions, about half had writing flaws and about one in five had a factual or conceptual error, yet nine in ten were a reasonable first draft for an expert to fix. One subject, 65 questions, one tool at one point in time.
Camarata and colleagues, “LLM-generated multiple choice practice quizzes for preclinical medical students”, Advances in Physiology Education 49(3), 2025; read on 14 November 2026In a 2026 comparison of 100 AI-written and 100 expert-written exam questions, the AI set had about twice the rate of writing flaws and was worse at telling strong candidates from weak ones. One medical specialty and one tool.
Feizi and colleagues, British Journal of Ophthalmology 110(7), a comparison of AI-written and faculty-written multiple-choice questions, 2026; read on 14 November 2026In a randomised trial at Harvard, 194 physics students learned more than twice as much from a carefully designed AI tutor as from an active-learning class, in less time. One course and one purpose-built design; its authors do not claim that it will hold everywhere.
Kestin and colleagues, “AI tutoring outperforms in-class active learning”, Scientific Reports 15:17458, 2025; read on 14 November 2026As read on the European Commission’s AI Act page on 14 November 2026: the EU AI Act already requires organisations that use AI to build their staff’s AI literacy, and already bans AI that infers people’s emotions at work or in education. It treats AI that scores learning outcomes, or that feeds decisions about people’s jobs, as high-risk, with those rules due to apply from December 2027. Whether it covers a given tool is a question for your legal lead.
European Commission, “AI Act”, Shaping Europe’s digital future (policy page), Read on 14 November 2026Each module ends with a one-page job aid and one change made to a real piece of your own work. Notice how differently the next finished-looking AI draft reads once you know where its errors hide. We do not promise results at work: those depend on what you and your organisation do next.
Turn a training request into a checked statement of the performance gap, its causes and the share of it that training can fix.
Brief an AI tool for a learning design and audit what comes back against retrieval, spacing, guided practice and manageable load.
Run an accuracy and level check with a source pack and a subject expert, starting with numbers, steps, safety and names.
Find cues and wrong answer keys, check fit to the objective, and repair or retire each question before any score counts.
Write rules for an AI coach or role-play so that learners commit first and get hints before answers, and judge it by an unaided check.
Write a pathway rule from a pre-check of the real task, and limit how skills data is inferred, shared and used.
Write a one-page evaluation plan with a baseline and a comparison, and report what learning contributed, with no invented return figure.
Write a one-page rule set for your team: what may go into AI tools, who reviews what, what learners are told and how access is checked.
The pathway runs in order: a baseline assessment, then eight modules (the course everyone finished; six hours into twenty minutes; forty pages, seven errors; sixty questions; the coach that gave the answer; not a visual learner; seven points, not nine; ready to roll), and a final assessment of 20 questions set in workplaces other than the story’s.
Measured before you start and again on the same instrument at the end, so the change is recorded rather than claimed.
The baseline assessment, Form A: 20 scenario questions on all eight competencies, about 25 minutes. It is not pass/fail; it sets your baseline, and you take the parallel form at the end.
Turn a request for a course into a performance gap with a measure, sort the causes, and use AI on needs data safely. Video: The iceberg under the request. Scenario Sim: What the reports really say.
Design on retrieval, spacing, guided practice and manageable load, then brief an AI tool and audit its draft. Video: One long night or eight short visits. Scenario Sim: Eight sessions.
Ground an AI tool in a source pack, check claims in order of harm with a subject expert, and fit the level to one reader. Video: Fluent is not the same as true. Scenario Sim: Before it ships.
What a question is evidence of, the cues that let a guesser pass, and a four-pass review for AI-drafted questions. Video: The question that answers itself. Scenario Sim: Twenty-six flaws.
Design AI coaches, tutors and role-plays so that the learner thinks first, and judge them with the tool switched off. Video: The crutch and the coach. Scenario Sim: Sixty technicians.
Personalise on what people can already do, read skills data by its source, and write a one-page pathway rule. Video: Sort by what they can do, not by what they prefer. Scenario Sim: The vendor demo.
Plan the evaluation before you build, read a pilot against a baseline and a comparison group, and report what learning contributed. Video: Two lines and the gap between them. Scenario Sim: The slide for Adaeze.
Four gates before go-live: data, fairness, access and rights, at principle level, and a one-page rule set for AI in learning. Video: Four gates before go-live. Scenario Sim: Next week, please.
The final assessment, loaded from Form B: 20 parallel scenario questions, about 25 minutes. Score 70% or more (3 attempts) for the credential.
Finishing this pathway issues a credential backed by the evidence you produced along the way. It is verifiable by link and is also issued as an Open Badge 2.0, so an employer can check it without taking your word for it.
Module 1: sort job reports, survey comments and interview notes with an AI tool, strip personal data first, and hand-check a sample before you count. Practised in What the reports really say.
Module 2: a four-part brief and a four-question audit built on retrieval practice, spacing, guided practice and load. Practised in Eight sessions.
Module 3: a source pack, a claim check in order of harm, a level check for one named reader, and captions and text alternatives as you build. Practised in Before it ships.
Module 4: what a right answer proves, the cues that let a guesser pass, and a review in four passes: cues, key, fairness, trial. Practised in Twenty-six flaws.
Module 5: why performance with the tool is not learning, and three design rules: commit first, hints first, unaided check. Practised in Sixty technicians.
Modules 6 to 8: pathways without learning styles, the gap and not the rise, and four gates before go-live. Practised in The vendor demo, The slide for Adaeze and Next week, please.
What comes with the course, and what the practice is like.
What the reports really say, Eight sessions, Before it ships, Twenty-six flaws, Sixty technicians, The vendor demo, The slide for Adaeze and Next week, please. In each you make the team’s decisions and see what follows. They are browser simulations with a debrief: no AI tool is called.
One page per module, from the four-cause sort and the claim check to the pathway rule card and the four gates. They are Bodhih teaching models and are labelled as such.
Each module ends with checked links to universities, standards bodies, regulators and the major AI builders’ own free material. They are links to those sources’ own pages, not partnerships or endorsements.
Four complimentary Pro Kits from the Bodhih store, on training needs analysis and the annual plan, workshop design, training evaluation, and running an AI literacy programme.
Company seats with reporting on each learner’s progress and before-and-after assessment scores.
Ask about team seats| What is compared | A typical AI course for L&D | Applied AI: Learning and Development at Bodhih |
|---|---|---|
| Starting point | How AI works: machine learning and language processing theory, or a tour of tools. | The L&D job: needs, design, content, assessment, practice, pathways, measurement, governance. No coding. |
| Tools | A list of named products by phase, which dates when the products change. | Tool-neutral. The lessons teach the judgement that survives a change of tool and name no product. |
| The evidence | Popular L&D “facts” repeated unchecked, learning styles among them. | Each technique tied to named research with the date it was read, and well-known myths named as myths. |
| Practice | Theory quizzes, or live exercises on fixed dates. | Eight Scenario Sims and a task on your own work in every module. The sims are simulations, not live AI tools. |
| Price and time | Live certificates from professional bodies run to three days and well over two thousand US dollars; self-paced certifications cost about two hundred; some cohort programmes publish no price. | $99, about 11 hours, starting any day. Price, hours and the credential rule are stated openly. |
| Honest limit | Live programmes give you a facilitator, a cohort and, from professional bodies, recertification credits. | No live facilitator, no cohort and no outside accreditation. The videos are animated and narrated by a synthetic voice. |
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Straight answers on time, price, tools and the credential. If yours is not here, ask us and you will have a reply the same working day.
People who design, deliver, commission or measure workplace learning: L&D managers, instructional designers, trainers, HR partners, learning technologists and subject experts who train others.
No. There is no coding and no statistics beyond percentages. It helps to have tried an AI chat tool.
None by name. The lessons are tool-neutral, so what you learn still applies when your organisation changes tools. The Go deeper lists point to the major builders’ own free courses.
No. The labs are browser simulations: branching decision scenarios with a debrief. No AI tool is called and nothing you type leaves the course. You apply the ideas on your own tools in the Apply at work task of each module.
Nobody on camera. Each module has one animated concept video of about 75 seconds, narrated by a synthetic voice, with captions and a transcript.
No. You earn a Bodhih credential with an Open Badge 2.0 that you can share and that anyone can verify. It is Bodhih’s own credential. It is not accredited by an outside body, and we do not claim recognition that we do not have.
About 11 hours: eight modules of 75 minutes, a baseline assessment and a final assessment. At one module a week that is about eight weeks, and it is self-paced, so you may go faster. To earn the credential, complete all eight modules and pass the final (20 questions, 70% to pass, three attempts). There is no reviewed capstone.
The course is $99 for an individual, and $79 per seat for companies, with four Pro Kits included. The price shown on this page is a launch price, about half the regular price. The launch price ends on 10 January 2027, and after that date the regular price applies.
Universities, peer-reviewed journals, standards bodies, regulators and the AI builders’ own research. Every source was opened at the original and the date is recorded. Where a famous claim is weaker than its reputation, the course says so.
At principle level, with the date each source was read, and with the questions to take to your legal and data protection leads. It is not legal advice. For depth, see Bodhih Responsible AI and Privacy.
The learning science applies anywhere, and several studies in the course come from schools and universities. The examples, the running case and the tasks are about workplace learning.
The course is $99 for an individual, or $79 per seat for companies, with four Pro Kits included. The launch price ends on 10 January 2027. You can start today with the baseline assessment and Module 1, where the first question is not what to build but what the need is. For a team, write to solutions@bodhih.com or call +91 99000 11601.
Starting records where you stand today. That baseline is what the re-measurement is compared against, and it is never rewritten afterwards.
Four hours of hands-on AI literacy for every employee, with a measured before-and-after score.
Lead your team’s AI use safely and lawfully: the 2026 rules, three Scenario Sims and a real AI register entry.
Eight modules on judging claims, sources, numbers and AI answers before you pass them on, with one running case, an animated video and a browser simulation in every module.
Measure your team first, plan each person’s growth after, or bring a trainer or coach in. Bodhih’s sister companies cover the rest of the journey.