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Applied learning is explained as a method wherein knowledge is transferred to students through the direct application of the educators’ skills and expertise, which includes using the perspective of existing theories and models to ensure that learners (i.e., students) gain practical knowledge and skillsets of AI (Grajeda et al., 2023; Zawacki-Richter et al., 2019). Employing a holistic approach, applied learning helps to nurture the goals, objectives, talents, and abilities of learners. In the age of technological advancement, which brought about the emergence of AI, applied learning is considered a pragmatic approach to acquiring the technical skills of AI. AI, as an advanced and revolutionary technological system for teaching and learning in a modern educational system, is the combination of various interconnected technologies which ensures that a computer system with its robotic operations is able to assimilate, deduce, monitor, communicate, and as well make judgments which are considered equivalent to that of humans (Ahmad et al., 2024; Nassoura, 2022; Osolase et al., 2024).
As individuals who are responsible for using their knowledge and teaching expertise to transform the future of students in their chosen careers, applied learning is a tool that university lecturers utilize for developing the knowledge of students on AI-related skills and abilities (Ahmad et al., 2024; Fisher & Mittelman, 2013). However, for students to acquire knowledge of AI, which utilizes available data sets for effective operations, it must be practical and hands-on, such as employing the applied learning technique (Ogunleye et al., 2024). According to Nassoura (2022) and Zawacki-Richter et al. (2019), applied learning of AI comprises of various parts, which are knowledge management, using theories to interpret the phenomenon as related to skills and competencies gained, in addition to experiences acquired from practical learning. Applied learning enables educators (i.e., university lecturers) to apply their teachings and instructions to real-world contexts, thereby improving students’ knowledge and learning experiences on machine and deep learning, robotics, computer vision, natural language processing, automation, chatbots, virtual agents, speech recognition, etc.
For example, a study conducted by Brasca et al. (2022) revealed that educators at Western Governors University located in Utah, United States of America, developed early intervention programs that adopted predictive modeling to enhance the possibility of recognizing students who had problems in retaining knowledge. As a result of this, between 2018 and 2020, the percentage of students registered in programs with 4-year lifespans and who were able to complete their studies increased by 5% (Brasca et al., 2022). Apart from boosting the educators’ ability to become productive, the students’ knowledge and skillsets of AI improved significantly, which was because of the lecturers having to redesign their teaching components for enhancing the applied learning process (Brasca et al., 2022; Chui et al., 2018). With AI technologies such as ChatGPT deployed for applied learning that is interactive, skills related to critical thinking and problem-solving were developed among a wide range of students (World Economic Forum (WEF), 2023). In a similar study conducted by researchers in the WEF, it was disclosed that adaptive learning technology was used by educators across various higher education institutions (HEIs) in refining the development of AI knowledge and competencies of learners (WEF, 2023). The lecturers used data-driven instructions to modify and make the applied learning process meet the current and future demands of the students. As the consultants in WEF declared, on account of using the applied learning strategy, there was an improvement in the students’ foresight, awareness, and consciousness of AI technologies (WEF, 2023). Additionally, their critical thinking and problem-solving skills were improved.