An Overview of Artificial Intelligence-Enhanced Teaching Methods

Introduction

Artificial intelligence¹ (AI) pertains to the emulation of human intellect within computer systems or the creation of algorithms enabling robots to do tasks that conventionally necessitate human intelligence and academic approaches such as data science (Shafik, 2024a). These tasks involve a diverse array of activities, which include but are not limited to problem-solving, decision-making, audio and picture recognition, natural language understanding, and learning from data (Pheng et al., 2022). AI systems are specifically engineered to do comprehensive analyses of extensive datasets, discern recurring patterns, generate predictions, and dynamically adjust their behavior in response to the information they have processed (Yachen, 2022). The overarching objective of AI is to develop computers or software capable of demonstrating cognitive abilities comparable to human intelligence.

AI can be classified into two primary categories: narrow or weak AI and general or strong AI². Narrow AI³ refers to a type of AI that is specifically built to perform well-defined tasks, such as those involving virtual personal assistants, recommendation systems, and self-driving automobiles (Fan & Zhong, 2022). These systems demonstrate a high level of specialization and proficiency within their respective fields. However, they do not possess the broader cognitive capabilities that humans possess, and some AI challenges differ according to current technological acceptance and trust (Shafik, 2024b). On the other hand, the objective of generated AI⁴ is to develop robots that possess intellect comparable to that of humans, enabling them to comprehend and execute a diverse array of activities with the same level of adaptability as people (Wei et al., 2022; Shafik, 2024c). The concept of AI, although a prominent objective, is predominantly theoretical and has yet to be fully actualized.

More still, AI systems are constructed based on a variety of foundational technologies, which encompass machine learning, neural networks, natural language processing, and expert systems. Machine learning, which falls under the umbrella of AI, enables systems to acquire knowledge from data and enhance their performance through iterative processes without the need for explicit programming (Russell et al., 2023). Neural networks⁵, which draw inspiration from the structure and functioning of the human brain, serve as the fundamental framework for deep learning, a specialized branch of machine learning that has gained significant acclaim for its remarkable achievements in several domains, such as image and speech recognition. Natural language processing⁶ (NLP) enables robots to understand and produce human language, a crucial capability for applications such as chatbots⁷, virtual assistants, and language translation (Shafik, 2023; Abdalgane & Othman, 2023). Expert systems employ rule-based logic in order to replicate the decision-making procedures of human experts within particular fields.

AI exhibits extensive applicability across diverse sectors, encompassing education, healthcare, banking, autonomous vehicles, and manufacturing. The phenomenon possesses the capacity to bring about significant changes in our contemporary society, which is increasingly reliant on data. This completely transforms various aspects of our lives, professional endeavors, and interactions with technological advancements (Bilgic et al., 2022). The rapid expansion of AI capabilities necessitates thoughtful examination and regulation of ethical concerns, including algorithmic prejudice and its implications for employment, in order to safeguard the overall societal benefits of AI. With the ongoing progression of AI, the potential for addressing intricate challenges and enhancing human capacities arises, thereby envisioning a future characterized by the coexistence of people and intelligent machines in a mutually beneficial and interdependent manner (An, 2022).