Designed for students in Grades 8–12, this internship introduces learners to the world of market research with a strong focus on using generative AI tools to work more efficiently and effectively. Students will learn how to design surveys, gather and analyse data, identify trends, and understand consumer behaviour—while leveraging AI to generate insights, summarise findings, and create impactful reports and presentations. The experience blends analytical thinking with smart use of technology to deliver faster, more accurate outcomes.
International Certification from QAI, UK (NSDC Endorsed)
Industry Expert Session
Skill level
Beginner
Duration
2 weeks
Projects
2
Prerequisites
None
Why it matters:
In a data-driven world, the ability to use AI to streamline research and decision-making is a critical advantage. This internship helps students save time on repetitive tasks, improve the quality of their insights, and focus on strategic thinking. Early exposure to AI-powered research builds digital fluency, enhances productivity, and prepares students for future careers where efficiency, accuracy, and informed decision-making
Value Proposition
Students who complete an internship in AI tools that enrich learning will gain valuable skills applicable to several undergraduate programs, especially:
1
Education Technology
Practical experience with AI-driven tools provides a head start in understanding how technology is transforming the educational landscape.
2
Computer Science
Exposure to AI in education introduces foundational concepts in machine learning, data analytics, and software applications.
3
Psychology and Cognitive Science
Learning about adaptive and personalized learning tools aligns with studies in human behavior and cognitive development.
4
Business and Marketing
Knowledge of AI tools that engage learners can enhance skills in market research and user experience, important in edtech and training fields.
5
Data Science and Analytics
Practical data handling and interpretation skills gained from working with AI tools are highly beneficial in data-centric fields.