  Today, Machine Learning (ML) is of a great importance to society due to the
availability of huge data and high computational resources. This ultimately led
to the introduction of ML concepts at multiple levels of education including
K-12 students to promote computational thinking. However, teaching these
concepts to K-12 through traditional methodologies such as video lectures and
books is challenging. Many studies in the literature have reported that using
interactive environments such as games to teach computational thinking and
programming improves retention capacity and motivation among students.
Therefore, introducing ML concepts using a game might enhance students'
understanding of the subject and motivate them to learn further. However, we
are not aware of any existing game which explicitly focuses on introducing ML
concepts to students using game play. Hence, in this paper, we propose
ML-Quest, a 3D video game to provide conceptual overview of three ML concepts:
Supervised Learning, Gradient Descent and K-Nearest Neighbor (KNN)
Classification. The crux of the game is to introduce the definition and working
of these concepts, which we call conceptual overview, in a simulated scenario
without overwhelming students with the intricacies of ML. The game has been
predominantly evaluated for its usefulness and player experience using the
Technology Acceptance Model (TAM) model with the help of 23 higher-secondary
school students. The survey result shows that around 70% of the participants
either agree or strongly agree that the ML-Quest is quite interactive and
useful in introducing them to ML concepts.
