The Relationship Between Artificial Intelligence and Academic Integrity
The Relationship Between Artificial Intelligence and Academic Integrity
by: Aldrin N. Lanuza
As a student myself, I often use AI tools when doing my assignments, especially if it’s research where grammar and rules against plagiarism are very strict and must be followed. I admit that I use AI tools to some extent for my work to ensure that there’s no errors. Before we dive into our discussion, let us define different terms first. Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to do tasks commonly associated with intelligent humans. The term is commonly used to describe the process of developing systems with human-like intellectual processes, such as the ability to reason, discover meaning, generalize, or learn from prior experience (Copeland, 2024). On the other hand, the term "academic integrity" refers to a dedication to honesty, trust, fairness, respect, accountability, and bravery. Academic integrity is more than just a term; it is a set of ideals to be upheld (Lee, 2023). These two terms will be further elaborated in the following paragraphs including their relationship with one another.
Origin of Artificial Intelligence (AI)
Although exact dates are difficult to determine, the roots of artificial intelligence may most likely be traced back to the 1940s, specifically 1942, when the American science fiction writer Isaac Asimov published his short story Runaround. Around the same time, but over 3,000 miles apart, the English mathematician Alan Turing worked on considerably less imaginary challenges, developing a code-breaking computer dubbed The Bombe for the British government with the purpose of cracking the Enigma code used by the German army during WWII. The Bombe, which measured around 7 by 6 by 2 feet and weighed about a ton, is widely regarded as the first operational electromechanical computer. In 1950, he released his landmark work "Computing Machinery and Intelligence" detailing how to design intelligent computers and, more particularly, how to quantify their intelligence. This Turing Test is still used as a criterion for determining an artificial system's intelligence. Six years later, in 1956, Marvin Minsky and John McCarthy (a computer scientist at Stanford) organized the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) at Dartmouth College in New Hampshire for around eight weeks. This workshop, which started off the AI Spring and was funded by the Rockefeller Foundation, brought together men who would become known as AI's founding fathers (Haenlein & Kaplan, 2019).
Drake (1941), Bowers (1964), and McCabe (1992) are the people who have had the most significant and direct influence on the vast amount of current research on academic integrity. These authors focused on academic integrity, or its opponent, academic misconduct, rather than cheating in general or essential ideas such as moral growth. McCabe's research, the most extensive multi-institutional study ever conducted with 6,096 students at 31 colleges and universities, astounded him and those who represented the participating institutions so much that they established the Center for Academic Integrity (now the International Center for Academic Integrity or ICAI) to address the problem of cheating and improve integrity cultures in higher education institutions. 1992 is widely regarded as a watershed moment in the academic integrity field and movement. Google Scholar records 1,820 results for the keyword academic integrity between 1941 and 1991. However, since McCabe's first analysis in 1992, there have been 19,000 hits on the same term throughout the course of 28 years, till 2020 (Gallant & Rettinger, 2022).
Current Situation of Artificial Intelligence (AI)
According to the most recent Artificial Intelligence Index research, AI usage in major organizations surged by 47% in 2019 compared to 2018. According to the same research, worldwide private AI investment could possibly top $70 billion in 2022, indicating a strong interest in AI software and startup companies. Simultaneously, AI and related areas have drawn an increasing number of university students worldwide, making AI specialists among of the most sought-after professions in industrialized nations. AI has found its way into a variety of corporate applications, including customer relationship management, software, recruitment services, worker productivity, and resource planning tools. AI is emerging as a robust and adaptable technology, with applications ranging from search engine algorithms and customer care chatbots to workplace apps and solutions. Despite the price and other hurdles of effective implementation, AI has too much potential to be overlooked. Companies large and small have successfully used AI to streamline processes, analyze enormous data quicker, and decrease human mistakes and labor. The outcomes provide actual benefits to their consumers (Hanes, 2023).
According to Dontoh et. al., over the last two decades, online learning has grown gradually. However, the COVID-19 pandemic caused a major shift from in-person to online classrooms. This affects around 1.6 billion pupils (94% of the student population) in 190 nations (Paudel, 2021). Although online learning has many benefits, the rapid change has also presented obstacles. One prominent and disturbing difficulty in online learning is that academic dishonesty is seen to be more severe, sophisticated, and prevalent than in traditional classrooms. If these worries are genuine, they might have major ramifications for educational institutions' reputations, faculty job satisfaction, the integrity of graduate credentials, and even societal cohesiveness (Norris, 2019; Chirikov et al., 2019; Valizadeh, 2022). The weakening of established defenses against online cheating is a major topic raised in literature. Ghizlane and Reda (2019) underlined that the absence of continuous and automated monitoring systems, along with insufficient authentication mechanisms, offers a significant obstacle to online proctoring. Numerous studies have found increased temptations to cheat in online environments due to factors such as a lack of direct supervision, technical difficulties, a lack of effective proctoring, and a perceived decrease in commitment to upholding integrity. Furthermore, easy access to the internet has increased the motivation to cheat (Paullet, 2020).
Relationship Between Artificial Intelligence and Academic Integrity
With the widespread use of artificial intelligence in recent years, many opportunities and possibilities have opened in the field of science, business, and more, especially in education. The COVID-19 pandemic became the catalyst to make AI more known to ordinary people. When online classes became a norm, students could simply do their assignments with the help of AI such as Grammarly and ChatGPT. This situation became worse to the point that teachers had to find a way to counter the students using AI tools with another AI detector tools. For instance, ChatGPT. With just a simple prompt, it can create various things and answer complex questions in a way an individual wants it to. Like, when creating an essay, it can be done by ChatGPT with just a few clicks in a minute. It’s extremely convenient and helpful, especially if one has many things to do. However, this brings a lot of ethical questions when it comes to academic integrity of the students. The work of an AI is inarguably excellent and spotless. It’s so on point and brings a lot of ideas and information about the subject matter. This results in students using AI tools getting high scores in their assignments. Compare this to someone who does not use AI getting lower score than the previous student, does that raise any concern? AI tools are indeed helpful in many ways possible especially for students. The question now is how much should we consider when it comes to the use of AI tools to not violate any academic integrity? Well, that’s another topic to ponder about.
Human intelligence is a mental quality that includes the ability to learn from experience, adapt to new situations, comprehend and handle abstract concepts, and manipulate one's environment (Sternberg, 2024), while academic integrity encompasses more than just avoiding dishonest behavior such as test copying, plagiarism, or contract cheating; it also entails a commitment to learning and work that is well-done, thorough, and focused on a positive goal—learning. It also includes using appropriate instruments, exerting genuine effort, and demonstrating great talents. It mostly comprises taking full advantage of all available learning opportunities (Lee, 2023). Therefore, using AI tools when doing academic tasks does not only prevent its users from honing their comprehension, but it also hinders their learning capabilities and promotes dependence on technologies. Artificial intelligence and academic integrity are correlated with one another, with the use of one affecting the other. For instance, using AIs in academic-related tasks might impair an individual's academic integrity by increasing plagiarism, relying too heavily on technology, and creating ethical issues such as AI-generated works. However, it is crucial to note that this relationship is not absolute, and the impact of AI on academic integrity may differ depending on how it is implemented and managed inside educational institutions.
Conclusion
Sources:
Copeland, B. (2024, April 10). Artificial intelligence (AI) | Definition, Examples, Types, Applications, Companies, & Facts. Encyclopedia Britannica. Retrieved April 14, 2024, from
Sternberg, R. J. (2024, April 5). Human intelligence | Definition, Types, Test, Theories, & Facts. Encyclopedia Britannica. Retrieved April 14, 2024, from https://www.britannica.com/science/human-intelligence-psychology
Lee, C. (2023, May 25). What is academic integrity? | Academic Integrity Definition. Turnitin. Retrieved April 14, 2024, from https://www.turnitin.com/blog/what-is-academic-integrity-definition
Haenlein, M., & Kaplan, A. (2019). A Brief History of artificial intelligence: on the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
Hanes, M. (2023, May 15). The current state of AI. Marsner Technologies. Retrieved April 14, 2024, from https://marsner.com/blog/the-current-state-of-ai/
Dontoh, J., Annan-Brew, R. K., Kpodoe, I. A., & Dadzie, J. (2023). Navigating Academic Integrity in the digital Era: Challenges, strategies, and solutions. IISTE. https://doi.org/10.7176/jep/14-25-05
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