Background of the Problem Statement
Enrolment and retention in vocational training in India is shaped as much by family perception as by the learner's own interest. Vocational pathways are frequently seen by parents as a lower-status alternative to academic/degree routes, and this perception - more than access or affordability alone - contributes to low enrolment, mid-course dropout, and reluctance to pursue NSQF - aligned progression. Existing career-guidance tools in the skilling ecosystem are almost entirely learner-facing; they help a student choose a course but do little to inform or reassure the parents who often make or veto that decision, particularly in rural and semiurban households. There is a need for a counselling tool that engages both the learner and the family unit together, addressing parental concerns about earning potential, safety, and social standing of a trade with credible, localised, data-backed information, rather than treating career guidance as a purely individual, learner-only exercise.
Description of the Problem Statement
The challenge is to build an AI-enabled counselling platform that can:
Engage learners and parents jointly through a conversational interface in regional languages, addressing common parental objections (income potential, job security, social perception) with credible local data. Present verified outcome data for specific trades and training providers - average posttraining earnings, placement rates, and progression pathways (NSQF level-ups, further education routes). Offer a structured, low-jargon explainer of how vocational qualifications map to job roles and career growth, tailored to the family's own context (location, household income bracket, learner's academic background). Provide human-escalation options (connecting to a live counsellor) for cases the AI cannot adequately resolve. Track engagement and sentiment shifts to help scheme administrators identify where parental resistance is concentrated.
Expected Solutions / Outcomes
A working conversational counselling tool usable by both learners and parents, in at least one regional language plus English. A verified outcome-data backend (placement rates, earnings ranges) that the tool draws on rather than generic content. A dashboard for scheme administrators showing where and why family resistance is concentrated. Evidence of design for low-literacy and low-digital-familiarity users. A clear escalation path to human counsellors for unresolved concerns.