Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success

“Encouraging active learning Active learning methods, such as problem-based learning, can help students gain a deeper understanding of the theoretical concepts and their practical implications (Pirker et al. 2014; Prince 2004).”

“As AI-driven marketing analytics becomes more wide-spread, it is essential to explore the ethical dimensions of these techniques (Mittelstadt et al. 2016).”

“Designing collaborative learning activities Group projects, discussions, and other collaborative learning activities can help students develop critical thinking and problem-solving skills by exposing them to diverse perspectives and approaches to problem-solving (Johnson and Johnson 2009).”

“…Instructors should incorporate case studies and real-world examples of sentiment analysis applications in marketing to showcase how companies are leveraging sentiment analysis to monitor brand reputation, identify areas for improvement, and tai-lor marketing messages (Huang and Rust 2021).”

“These developments have increased the need for marketing educators to adapt their curricula and teaching strategies to reflect the rapidly changing landscape of marketing analytics (Lamberton and Stephen 2016).”

“Using scaffolded learning techniques Scaffolded learning techniques, such as guided instruction and worked examples, can help students develop problem-solving skills by gradually increasing the complexity of tasks and the level of independence required to complete them (Kim et al. 2022).”