The integration of Artificial Intelligence, or AI, in academics has emerged as a promising avenue for revolutionizing traditional teaching and learning methods as well as optimizing administrative processes within educational institutions. However, the adoption of AI presents both challenges and opportunities. This case study delves into the rationale behind utilizing AI in the academic realm, explores the details of interaction with AI tools as the overlooked search engines that they can be, discusses potential outputs, and addresses concerns associated with their implementation. The overall use of AI in academics has been met with much scrutiny and disapproval, but integrating it into the collegiate environment offers creative opportunities and learning experiences and streamlines academic processes, ultimately leading to improved academic outcomes and efficiency. For this report, I will be analyzing my use of AI as an optimized search engine.
The AI Issue
Academic integrity is a critical transdisciplinary skill for academic success (Khalaf, 2024). Educators have various concerns about the integration of AI into the classroom because of that. There are ethical worries surrounding AI’s potential biases and its impact on student privacy as well as concerns about equity, as not all students may have equal access to AI tools, creating disparities in learning outcomes. There is a fear that AI might replace or diminish the role of human teachers, leading to job insecurity or devaluation of teaching expertise. Additionally, some worry that excessive reliance on AI could stifle critical thinking skills and creativity among students. There’s also skepticism about the reliability of AI-generated content and assessments and concerns about data privacy and security. While recognizing AI’s potential benefits, there is emphasis placed on the need for cautious implementation to ensure it enhances, rather than detracts, the quality of education.
The Overlooked Search Engine
While dealing with academic work, AI is used for numerous reasons. Many may use it as a source for direct answers to whatever problem they may have. The final product submitted is what draws concerns, however. This is where the black and white of this topic becomes shades of grey. While there are many reasonably placed concerns about AI and its potential, they often overshadow the main use of AI, which is as a tool for generating new and diverse content that can be used for a wide range of applications, from creative endeavors to scientific research to entertainment. Its ability to create realistic-looking content opens up numerous possibilities for innovation and exploration in various fields.
When completing coursework, it is important to use all tools and materials at your disposal because it enables you to work more efficiently, gain a deeper understanding of the subject matter, foster creativity and innovation, produce higher-quality output, and prepare yourself for future academic and professional challenges. This includes AI as well. AI can be used as the ultimate time saver when it comes to research and gathering information. When you use a typical search engine such as Google or Bing, or even an academic database like the college library, Google Scholar, IEEE Xplore, JSTOR, Web of Science, or ERIC (Education Resources Information Center), you are often limited by matching keyword results. For example, if one searches for “What is Maslow’s Hierarchy of Needs?” one will get a simple answer of “From the bottom of the hierarchy upwards, the needs are: physiological, safety, love and belonging, esteem and self-actualization,” which is correct. To get more in-depth answers, you would need to go through the links on the following pages to gather the information. If prompting a form of AI, in this case ChatGPT, you will get the following:
What is Maslow’s Hierarchy of Needs?
Maslow’s Hierarchy of Needs is a theory in psychology proposed by Abraham Maslow in his 1943 paper “A Theory of Human Motivation.” It suggests that humans have a hierarchy of needs, and as lower-level needs are fulfilled, individuals progress toward meeting higher-level needs. The hierarchy is often depicted as a pyramid, with the most fundamental needs at the base and higher-order needs at the top. Maslow’s original hierarchy consists of five levels:
Physiological Needs: These are the basic, biological needs required for human survival, such as air, water, food, shelter, sleep, and reproduction. These needs must be met before an individual can progress to higher levels of the hierarchy.
Safety Needs: Once physiological needs are satisfied, individuals seek safety and security. This includes physical safety, financial security, health, stability, and protection from harm, danger, or threat.
Love and Belongingness Needs: After safety needs are met, individuals crave social connection, love, and a sense of belonging. This involves forming intimate relationships, friendships, family bonds, and being part of social groups or communities.
Esteem Needs: Once lower-level needs are fulfilled, individuals strive for self-esteem and the esteem of others. This includes developing self-confidence, gaining respect from others, achieving recognition, and feeling a sense of accomplishment.
Self-Actualization: At the top of the hierarchy is self-actualization, which represents the fulfillment of an individual’s potential and the realization of personal growth, creativity, and fulfillment. Self-actualized individuals are driven by a desire for personal growth, autonomy, and the realization of their unique talents and capabilities.
Maslow later expanded his hierarchy to include additional levels, such as cognitive needs (knowledge, understanding), aesthetic needs (appreciation of beauty and symmetry), and self-transcendence (spiritual fulfillment, connection to something beyond oneself). However, the original five-level hierarchy remains the most widely recognized and influential aspect of Maslow’s theory.
While AI is not to be trusted 100% when it comes to the information it presents, the output that comes from it is an excellent starting point for any assignment. It can be looked at as an anchor point or a reference for gathering potential scholarly information. In this next scenario, we will try the same prompt both in a typical Google search as well as an AI search, this time using Claude AI.
What is color theory? Can you give me a peer-reviewed article?
The first search result is from the National Library of Medicine. It is an article entitled “Color and Psychological Functioning: A Review of Theoretical and Empirical Work” and it is listed in its full entirety. While this is packed with information, it is not the information that is needed. If you go back to the result page, the next prompt on the page is “What is the color theory?” followed by the answer “Color theory asserts three pure primary colors that can be used to mix all possible colors. These are sometimes considered as red, yellow, and blue (RYB) or as red, green, and blue (RGB).
This is a correct answer, but it was not the first prompt and to someone who doesn’t have an inkling about what color theory may be, they may be lost in the weeds from that first article. Now we will prompt Claude AI with the same question. Claude AI is an artificial intelligence chatbot much like ChatGPT, but with additional options so you can converse with Claude using natural language, just as you would with another person. Claude can generate various forms of text content such as summaries, creative works, and code. You can also upload images and text-based files to add context to your prompts.
What is color theory? Can you give me a peer-reviewed article?
Color theory is a set of principles and guidelines used to understand how colors relate to one another, how they can be combined effectively, and how they impact human perception and emotions. It explores the relationships between colors and provides a framework for creating harmonious color combinations or evoking specific moods and meanings through color choices.
Here is a brief overview of some key concepts in color theory:
Color wheel: Arrange colors in a circular format based on their chromatic relationships, with primary, secondary, and tertiary colors.
Color harmony: Principles like complementary, analogous, triadic, etc. that suggest which color combinations are aesthetically pleasing.
Color context: How colors are influenced by their surroundings and the colors they are paired with.
Color psychology: The study of how different colors can evoke particular emotions, associations, and responses in viewers.
Color symbolism: The cultural meanings and symbolic associations attached to different colors across societies.
A peer-reviewed article on color theory:
Labrie, D. (2020). Extending color theory in three dimensions: A critique of the CIELAB color space. Anthropology of Color: Interdisciplinary Multilevel Modeling, 439–455. https://doi.org/10.1075/z.137
This article critiques the widely used CIELAB color space model and proposes extending color theory into three dimensions to better represent human color perception and experience. It was published in the book “Anthropology of Color: Interdisciplinary Multilevel Modeling” by John Benjamins Publishing Company.
Output Analysis & Plagiarism Concern
Even with the most descriptive output, AI can go in multiple directions regarding its output. While the output can go multiple ways, it can still be used as a stepping stone to get to the final solution. As seen in the example above, the AI answer output was more thorough and more explanatory as compared to the typical search engine response. If this question were presented to a student, this would be a fantastic starting point. On top of that, it covers a lot of information with one prompt, if stated correctly. This information would need to be corroborated with a more scholarly result or database, but it gives a great point of entry to the information.
The main issue that arises from this concept is plagiarism. An article published in the European Conference on the Impact of Artificial Intelligence and Robotics believes that students can differentiate between using generative AI in an academically acceptable way. Francke & Bennett (2019), believe that AI provides students with the capability to use it to complement their academic efforts in producing their academic work, instead of submitting the results of the generative AI as their work. Overall, the level of belief that students will automatically plagiarize falls on the instructors. As Yeo (2023) explains, much like with the implementation of any technology, it is not going anywhere and it will be difficult, if not impossible, to unring that bell. Instead of attempting to limit the technology, instructors should accept it and adapt their curriculums and assignments to instruct students on how to use generative AI ethically to achieve their learning outcomes.
Conclusion
As AI continues to reshape the landscape of higher education, it is imperative to reframe the narrative surrounding its usage in college coursework. Rather than viewing AI solely as a source of cheating, it should be recognized as a multifaceted academic tool that can empower students to achieve their full potential and thrive in a rapidly evolving digital world. By promoting responsible, ethical usage and integrating AI into the academic environment thoughtfully, colleges and universities can harness its transformative potential to enrich teaching and learning experiences. There should be no tool unavailable for the student that could potentially expand their horizon when completing academic work.
References
Francke, E., & Bennett, A. (2019,). The potential influence of artificial intelligence on plagiarism: A higher education perspective. In European Conference on the Impact of Artificial Intelligence and Robotics (ECIAIR 2019) (Vol. 31, pp. 131-140).
Khalaf, M.A. (2024). Does attitude towards plagiarism predict plagiarism using ChatGPT? AI Ethics. https://doi.org/10.1007/s43681-024-00426-5
Yeo, M. A. (2023). Academic integrity in the age of artificial intelligence (AI) authoring apps. Tesol Journal, 14(3), e716. https://doi.org/10.1002/tesj.716