Elementary Level
Artificial Intelligence (AI) has become an essential part of our daily lives, especially throughout our academic journey. We rely on it for studying, research, assignments, and even communication.
This guide is designed to support the effective and ethical use of AI while completing academic tasks. It aims to help learners understand basic AI terminology, avoid common mistakes, and apply practical tips and strategies to improve the quality of their work and enhance their learning experience.
Firstly, learning AI terminology supports building a foundational understanding and prevents confusion. It also enables clearer communication with AI tools, allowing for more effective and accurate use.
Artificial intelligence (AI) is a technology that enables computers and machines to simulate human learning, comprehension, problem-solving, decision-making, creativity, and autonomy. (IBM, 2025)
Examples: Alexa, Google Assistant, and chatbots represent simple implementations of artificial intelligence. These systems use AI techniques to understand user input, respond to questions, and perform basic tasks by simulating human interaction.
Phrase Information: check the videos below
Machine learning is a branch of artificial intelligence that enables algorithms to uncover hidden patterns within datasets. It allows them to predict new, similar data without explicit programming for each task. Machine learning finds applications in diverse fields such as image and speech recognition, natural language processing, recommendation systems, fraud detection, portfolio optimization, and automating tasks (geeksforgeeks, 2025)
Natural language processing (NLP) is a subfield of computer science and artificial intelligence (AI) that uses machine learning to enable computers to understand and communicate with human language. (IBM, 2025)
Recommender Systems are tools that suggest items to users based on their behaviors, preferences, or past interactions. They help users find relevant products, movies, songs, or content without manually searching for them and improve content discovery. (geeksforgeeks, 2025)
Machine learning serves as the foundational technology for both recommendation systems and natural language processing. There is integration between these technologies. the following Figure (1): Machine Learning (Badya University Library, 2026)
Examples of technology integration: Gmail Smart Reply, Outlook Suggested Replies, and Google Search Suggestions represent practical implementations of machine learning. (geeksforgeeks, 2025)
Phrase Information: check the videos below
Generative AI a type of artificial intelligence designed to create new content such as text, images, music, or even code by learning patterns from existing data. (geeksforgeeks, 2025)
Examples: Microsoft Designer, Runway, Open Art, Artbreeder, and ChatGPT. These tools use AI models to generate visual content such as images and text.
Phrase Information: check the videos below
To compare between these terminologies and know more: check the videos below
AI Hallucinations are similar to how humans sometimes see figures in the clouds or faces on the moon. In the case of AI, it is a phenomenon in which a generative AI chatbot or computer vision tool perceives patterns or objects that are nonexistent or imperceptible to human observers, creating outputs that are nonsensical or altogether inaccurate (IBM, 2025).
Prompt Engineering is the process of creating clear and effective prompts that guide AI models to generate accurate responses. It mainly focuses on writing smart prompts for text-based AI tasks to help the user and the model produce the required output. (geeksforgeeks, 2025)
Phrase Information: check the videos below
Although AI tools can be highly beneficial, they also carry a risk of errors and inaccuracies. In some cases, AI systems may generate false or misleading information, a phenomenon known as hallucination. This happens when an AI model produces responses that appear plausible but aren’t factually correct or aren’t based on reliable data.
Such issues can arise for several reasons, including the following:
These challenges can be reduced by using prompt engineering, which helps AI give accurate and reliable results.
Using AI ethically requires an understanding of both the capabilities and limitations of AI tools. Ethical academic use involves respecting institutional policies and avoiding plagiarism.
As previously mentioned, the risks of fake information, hallucinations, and plagiarism are major challenges when using AI. Prompt engineering directly addresses these problems by guiding AI to generate accurate, reliable, and well-structured responses.
There are several techniques that can be used to improve prompts and help AI produce more accurate answers:
This guide is designed to support you throughout your academic journey. Accordingly, a checklist has been developed to assist at different stages of acadmic work: before you start, during the work process, and after you finish.
The following Figure (2): Preparatory Phase (Badya University Library, 2026)
Preparatory Phase: Before Starting Your Work
The following Figure (3): Implementation Phase (Badya University Library, 2026)
Implementation Phase: During your work
The following Figure (4): Evaluation and Review Phase (Badya University Library, 2026)
Evaluation and Review Phase: After you finish your work
For more information on how to cite references and generative AI, visit the citation and reference page.
The following Figure (5): AI Check list (Badya University Library, 2026)
Artificial Intelligence (AI) ethics refers to a set of moral principles and standards that guide the development and use of AI technologies. In the academic context, AI ethics plays an important role in ensuring that AI tools are used in ways that respect human values and protect individual rights.
It supports students and researchers in using AI tools in accordance with academic integrity and social responsibility.
AI ethics includes data responsibility and privacy, value alignment, trust, and the prevention of technology misuse. These principles are especially relevant in education and research, where AI tools are increasingly integrated into learning environments and support writing and analytical processes.
Note: To use AI tools ethically, check the checklist that has been created to support you.
Responsibility:
When deciding to use AI tools in assignments or research, responsibility is assumed for how they are applied. As previously mentioned, academic rules must be followed, AI tools should be used intelligently to avoid plagiarism, and it should be recognized that AI serves only as an assistant to support academic work.
For more information on how to cite references and generative AI, visit the citation and reference page.
To know more about AI ethics:
Version: 1
Date: 1st January, 2026
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