State of the Art of Artificial Intelligence and Autonomous Systems

Artificial Intelligence is the general term for the science of artificial intelligence. It uses computers to simulate human intelligent behaviors and it trains computers to learn human behaviors such as learning, judgment, and decision-making. AI is a knowledge project that takes knowledge as the object, acquires knowledge, analyzes and studies the expression methods of knowledge, and employs these approaches to achieve the effect of simulating human intellectual activities. AI is a compilation of computer science, logic, biology, psychology, philosophy, and many other disciplines, and it has achieved remarkable results in applications such as speech recognition, image processing, natural language processing, the proving of automatic theorems, and intelligent robots. AI plays an indispensable role in social development, and it has brought revolutionary results in improving labor efficiency, reducing labor costs, optimizing the structure of human resources, and creating new job demands.

AI is a multidisciplinary technology, one with the capability of integrating cognition, machine learning, emotion recognition, human-computer interaction, data storage, and decision-making. Per noted by Sarker (2022), the various technologies related to Artificial Intelligence can be broadly classified into 3 main areas: Sense, Comprehend, and Act. Sense refers to the perception of the surroundings. This includes the acquisition and processing of sounds, images, speech, etc. Comprehend describes the analysis of the collected information. Lastly, Act represents the physical manifestation of computer language instructions.

It was first proposed by John McCarthy at the Dartmouth Conference in the mid-20th century and encompasses various technologies such as machine learning and deep learning. Machine Learning is a way of achieving Artificial Intelligence. The phrase was first coined by Arthur Samuel in 1959. He defined it as the ability to learn without being explicitly programmed. Machine Learning enables Artificial Intelligence without hardwiring multiple complex rules and decision trees using millions of lines of code. Instead, this is done by implementing an algorithm that trains a machine on how to learn by itself so that it can adapt to upcoming changes without human intervention. This training is accomplished by feeding large amounts of relevant data, allowing the algorithm to adjust itself and improve.

Deep Learning is a technique to implement Machine Learning. The working of a Deep Learning algorithm is analogous to the working of an actual brain, with the use of multiple layers of neurons and each layer containing multiple neurons, which are connected using Artificial Neural Networks (ANNs). Each layer of an ANN picks out a feature to learn and multiple such layers are thus able to learn the whole information.  

In higher education, the introduction of Artificial Intelligence is often a catalyst, per stated by McDonald et al., (2024), for significant changes in teaching and learning. The overall evidence on the role of computing technology, (Cuban 2003), plays on the advance and improvement of quality in teaching and learning experience. For example, for educators and noted by Dai & Ke (2022) the use of Artificial Intelligence in educational application such as simulation-based learning, curriculum design, writing and coding among others, had ample the need for researchers to investigate the use of AI and impact in higher education, with special resonance of ChatGPT application.

Generative Pre-Trained Transformer (GPT) was released in 2018 by an OpenAI (San Francisco, California) as a type of Large Language Model (LLM) that aims to replicate human language processing capabilities (Cascella et al., 2023).  GPT leverages deep learning and powerful algorithms to perform various languages related tasks such as text generation, question answering, and translation, while comprehending the context to produce responses that resemble human language (Lund et al., 2023).

ChatGPT large language model trained to generate humanlike text based on a given prompt or context (Villareal et al., 2023).  It can be used for a variety of natural language processing tasks, such as text completion, conversation generation, and language translation” (Baidoo-Anu & Owusu, 2023, p. 4). Given its advanced generative skills, one of the major concerns in higher education is that it can be used to reply to exam questions, write assignments and draft academic essays without being easily detected by current versions of anti-plagiarism software (Zhai, 2022). The use of ChatGPT had created a mixed reaction among higher education and researchers. In one side of the spectrum, those claiming the emerging threat of academic integrity and in the other side those embracing it by publishing guidance and policies among students, faculty and staff members.

Among the key issues regularly cited related to ChatGPT in education are accuracy, reliability and plagiarism. (Lim et al., 2023 & Kasneci et al., 2023). Issues related to accuracy and reliability include relying on biased data (i.e., the limited scope of data used to train ChatGPT), having limited up-to-date knowledge (i.e., training stopped in 2021), and generating incorrect/fake information (e.g., providing fictitious references) (Lo, 2023). It is also argued that the risk of overreliance on ChatGPT could negatively impact students’ critical thinking and problem-solving skills (Lim et el., (2023). Regarding plagiarism, evidence suggests that essays generated by ChatGPT can bypass conventional plagiarism detectors (Lo et al., 2023). ChatGPT can also successfully pass graduate-level exams, which could potentially make some types of assessments obsolete (Kasneci et al., 2023).

Meanwhile, those embracing AI argue that ChatGPT may enhance education by providing tools to students and faculties to facilitate the learning and teaching experience. For example, for on the student side ChatGPT can be used to generate answers to theory-based questions and generate initial ideas for essays (Lim et al., 2023 & AlAfnan et al. 2023), but students should be mindful as noted by Villareal et al., 2023, of the need to examine the credibility of generated responses. Given its advanced conversational skills, ChatGPT can also provide formative feedback on essays and become a tutoring system by stimulating critical thinking and debates among students (Farrokhnia et al., 2023). The language editing and translation skills of ChatGPT can also contribute towards increased equity in education by somewhat leveling the playing field for students from non-English speaking backgrounds (Lim et al., 2023).

ChatGPT can also be a valuable tool for educators as it can help in creating lesson plans for specific courses, developing customized resources and learning activities (i.e., personalized learning support), carrying out assessment and evaluation, and supporting the writing process of research (Rahman et al., 2023). ChatGPT might also be used to enrich a reflective teaching practice by testing existing assessment methods to validate their scope, design, and capabilities beyond the possible use of GenAI, challenging academics to develop AI-proof assessments as a result and contributing to the authentic assessment of students’ learning achievements (Wiggins 2011).

In general, and with the limited studies and high demand of Artificial Intelligence tools, such as ChatGPT in higher education, challenges and opportunities are presented in a day-to-day basis. A more in deep knowledge and discussion needs to emerge collectively, that may provide better insights in how to overcome the challenges and embrace the opportunities that tools such as ChatGPT is providing, shifting the perspective as an object to support higher education learning experience instead of a subject to study.

References

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