Abstract

With the rapid advancement of generative AI technologies, such as ChatGPT, Gemini, and Copilot, concerns have arisen within the academic community regarding their potential impact on the learning process. There is a prevailing apprehension that these sophisticated AI tools may facilitate academic dishonesty by enabling students to complete assignments or answer questions without engaging in the necessary independent research. As a result, many universities have implemented strict bans on accessing these platforms on campus, others adopt more nuanced approaches. These varied responses underscore the complexity surrounding the incorporation of GenAI into educational paradigms, reflecting the diverse perspectives and strategies adopted by universities in navigating the transformative potential of emerging technologies. In this research study, we are embarking on an exploration of innovative pedagogical approaches that incorporate generative AI tools into the educational landscape, particularly within the domain of data analytics and programming. This endeavor represents a proactive response to the evolving technological landscape and seeks to leverage emerging tools to enrich the learning experiences of students. One facet of Our investigation includes using Google Colab's built-in GenAI feature, known for its accessibility and reducing barrier of entry on coding related tasks to novice users. We start by teaching students basic Python syntax over two weeks, then assign tasks to reinforce their understanding of programming fundamentals like conditional statements and loops. After completing assignments, students engage in interactive sessions where they apply GenAI practically to tasks similar to their assignments. This hands-on experience demonstrates GenAI's capabilities for code generation, aiding rapid prototyping and problem-solving. It also encourages critical thinking as students evaluate GenAI outputs against expected results, emphasizing the importance of discernment and validation in AI-driven solutions. In addition to these instructional activities, we are also exploring the integration of generative AI tools as a preliminary consulting solution for student-led data analytics projects. Leveraging datasets sourced from online platforms such as Kaggle.com, students employ GenAI to automate routine data preprocessing tasks, including data cleaning and basic descriptive statistics. By streamlining these initial stages of the data analysis pipeline, students can allocate more time and resources to higher-order analytical tasks, thereby enhancing the efficiency and efficacy of their project workflows. Through these multifaceted initiatives, our study seeks to assess the impact of integrating generative AI tools on student learning outcomes, computational proficiency, and project success rates. By empowering students with access to cutting-edge AI-driven technologies and fostering a culture of experimentation and innovation, we aim to cultivate a cohort of digitally fluent learners equipped to navigate and contribute to the rapidly evolving landscape of data science and technology.

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