Making Career Choices and AI Based Counselling Accessible to Every Child at Secondary Level Along with Aptitude Tests and Detailed Career Paths

Abstract

Career decision-making is a critical phase in a student’s life, often influenced by societal pressure, lack of guidance, and limited access to personalized counseling. This paper presents an AI-based Career Recommendation System designed to assist secondary and higher secondary students in identifying suitable academic streams and career paths based on their interests, goals, and personality traits. The system uses a machine learning-based classifier trained on student response data to recommend career domains and integrates a real-time chatbot for anonymous career queries and emotional support. The platform ensures privacy, promotes awareness, and empowers students to make informed career choices aligned with their aspirations.

Introduction

Career decision-making is a crucial yet challenging phase for secondary and higher secondary students, often complicated by limited self-awareness, anxiety, and lack of accessible counseling—especially in rural or under-resourced areas. Traditional career guidance methods can be inaccessible, impersonal, and stigmatizing.

To overcome these challenges, the project proposes an AI-based Career Recommendation System that uses machine learning and conversational AI to offer personalized, 24/7, private career guidance. The system employs structured quizzes tailored for 10th and 12th graders to assess interests, personality traits, and aspirations, recommending suitable academic streams or career paths accordingly.

An intelligent chatbot powered by OpenAI and Botpress allows real-time, anonymous conversations to clarify doubts, explore alternatives, and provide emotional support, enhancing engagement and reducing stress. The web-based platform is designed for easy access on low-end devices, requiring only internet and a browser, making career counseling widely available.

The system uses a Random Forest classifier trained on a curated dataset, integrates AI chatbots for interactive support, and follows ethical principles including privacy and fairness. It aims to democratize career guidance, improve decision confidence, and provide ongoing support with industry-relevant, personalized recommendations.

Conclusion

This AI-driven career counselling system represents a transformative step forward in how we guide students in making career choices. By leveraging the power of artificial intelligence, aptitude testing, and real-time chatbots, the platform provides personalized, data-driven recommendations that are both accessible and scalable. Unlike traditional career counselling methods, which often lack personalization and accessibility, this system offers a tailored approach that adapts to each student\'s unique skills, interests, and aspirations. The system’s integration of an Explore Careers section allows students to delve deeply into various professions, providing essential insights on the skills, educational paths, and job market trends for each career option. The inclusion of an AI-powered chatbot enhances user engagement by offering instant, interactive career guidance and alternative suggestions, ensuring that students receive continuous support throughout their decision-making process. Ultimately, this platform aims to empower students with the knowledge and tools they need to make informed, confident decisions about their futures. By improving accessibility, ensuring scalability, and offering real-time, data-driven insights, this system not only addresses the shortcomings of traditional methods but also paves the way for a more efficient, inclusive, and effective career counselling solution for students worldwide.

References

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Copyright

Copyright © 2025 Nishad Babu Sulikeri, Dinesh Kumar Reddy M, Manishimha G, Ashishika Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.