Students are asking about AI. Employers are asking students about AI. Many colleges have not yet had a structured session that gets students from curiosity to a first real result. This guide shows how to plan one for 2027.
What is an AI readiness session?
It is a campus event that introduces students to AI tools in a hands-on way and connects them to what it means for careers. The format is already common in India. The Computer Society of India's CSI AI 100K programme, for example, describes itself as an AI readiness program and has run its "AI Readiness Session" at engineering colleges. Other companies and colleges run their own versions, some as two-day workshops for CSE and AI/ML students.
Why now? Coverage of TeamLease's reporting says AI fluency is turning from a novelty into an expectation for freshers, and FACE Prep lists AI literacy among five areas freshers are screened on. A session is a quick way to move your students from awareness to practice.
Step 1: What outcome do you want?
Choose one, in a sentence. For example:
- "Every attending student builds or tests one small thing with an AI tool."
- "Fifty students start a project they will finish in 30 days."
- "Students can explain what AI can and cannot do, with examples."
A clear outcome decides the format, the speakers and how you will measure success.
Step 2: Which format fits?
| Format | Best for | Group size | What students leave with |
|---|---|---|---|
| Two-hour awareness session | Whole batch or several branches | 150 or more | A mental map of AI and careers, and a list of next steps |
| Half-day hands-on workshop | One branch or year | 40 to 80 | One small tool-assisted result |
| Two-day workshop | CSE, IT, AI/ML or interested volunteers | 30 to 60 | A working project and a short demo |
| Series (weekly, 4 to 6 weeks) | Committed students | 20 to 40 | A finished project, tested with users |
If your students have never used AI tools, start with the first format and offer the second to volunteers.
Step 3: What should the agenda look like?
Here is a sample for a half-day hands-on session. Adapt the timing.
| Time | Block | Purpose |
|---|---|---|
| 0:00 to 0:20 | Why this matters for your career | Set context with hiring facts and real examples |
| 0:20 to 0:50 | Live demo: from problem to working result | Show the process, including mistakes |
| 0:50 to 1:00 | Break | |
| 1:00 to 2:30 | Hands-on: students try it on their own problem | The main event, with mentors circulating |
| 2:30 to 3:00 | Checking AI output | Teach judgement: where AI is wrong, how to test |
| 3:00 to 3:30 | Show and tell, and next steps | Students share results and join a follow-up |
Keep talking to a minimum. Students remember what they make.
Step 4: Who do you need?
- A lead facilitator who can run a live demo and keep the room moving.
- One mentor for every 10 to 15 students during the hands-on block.
- A faculty coordinator to handle the room, attendance and follow-up with students.
- A placement cell contact to connect the session to recruiters and internships.
Step 5: What should you prepare?
Before
- Check Wi-Fi capacity and have a backup plan, such as phone hotspots.
- Decide which tools students will use and make sure free versions are available to all.
- Ask students to sign in to their accounts before the day.
- Prepare three simple problem prompts for students who are stuck.
During
- Start on time and keep to the plan.
- Ask students to work in pairs.
- Show a failure live and fix it.
After
- Share a one-page recap with links and next steps within 48 hours.
- Invite students to a follow-up programme.
- Send a short survey.
Step 6: How do you measure whether it worked?
Attendance shows interest. Impact shows in what students do later.
| Measure | How to collect it |
|---|---|
| Confidence and skill before and after | Two-question survey at the start and end |
| Projects started and finished | Follow-up form at two and six weeks |
| Students joining follow-up programmes | Sign-up list |
| Recruiter or internship interest | Placement cell notes |
What have we seen at Poditivity?
We run Build Theory AI readiness sessions on campuses. By our own records, 13 fellows came into the Build Theory Fellowship from a single session at MVSR Engineering College. That is one example from our own work, not a promise of any result, and it is why we plan every session around a next step: a 30-day project students can start straight away. Our 30-day portfolio project guide is the kind of follow-up we recommend.
What are the common mistakes?
- Too much talking and not enough building.
- No follow-up, so the energy fades in a week.
- Choosing tools that need paid accounts.
- Treating AI as only for CSE students. Every branch has problems AI can help with.
- Not teaching students to check what AI produces. See our AI skills roadmap.
Want a session at your college?
Poditivity can run an AI readiness session as part of Build Theory for Campus, and our platform 3E can help your placement cell track readiness afterwards. Write to contact@poditivity.com or use the Get in touch button on our home page.
Key takeaways
- Pick one outcome, then choose the format that matches it.
- Make it hands-on: students should leave having made something.
- Plan the follow-up before the session, not after.
- Measure projects started and finished, not just attendance.
- Teach students to check what AI produces.
Frequently asked questions
What is an AI readiness session?
It is a campus session, usually two hours to two days, that gives students a first hands-on experience with AI tools and shows them how AI changes careers and hiring. Good sessions end with students making something, not only listening.
How long should an AI readiness session be?
A two-hour session works as an introduction for a large audience. A one- or two-day workshop works better for hands-on practice with a smaller group. Many Indian colleges run two-day formats for CSE and AI/ML students.
How many students should attend?
Large halls work for awareness. For hands-on work, keep groups to about 30 to 60 with mentors circulating. If you have more students, run the same session in batches or add a short follow-up for those who want more.
How do we know if the session worked?
Track what students do afterwards: projects started, projects finished, applications to follow-up programmes, and a short before-and-after survey on confidence and skills. Attendance alone shows interest, not impact.
Sources
- CSI AI 100K: world's largest AI readiness program (Computer Society of India)
- India's entry-level hiring shifts as AI pushes faster learning curve: TeamLease (Storyboard18)
- Top skills that will get you a job (FACE Prep)
- Dell and Intel to launch AI Skills Lab in Telangana (Fone Arena)
Figures are quoted as reported by the sources above. Check the original report before reusing a number in your own work.