- Machine Learning (ML): The ability of machines to improve themselves using data and achieve a certain level of performance. Machine learning is the most widely used subfield of AI.
- Deep Learning: A subfield of machine learning that processes large amounts of data using neural networks, allowing for more complex inferences.
- Natural Language Processing (NLP): AI technology used to understand and generate text and speech data. Chatbots and language translation systems are examples of this.
- Knowledge Graphs: Visual representations of relationships between data and information. Knowledge graphs are used in a wide range of applications, from search engines to business intelligence.
- Autonomous Systems: Systems capable of making decisions independently and adapting to their environment. Autonomous vehicles are a striking example of this concept.
- Augmented Reality (AR): A technology that integrates digital information into real-world environments. AI-powered augmented reality applications enhance accuracy and usability.
- Artificial Neural Networks (ANNs): Mathematical models inspired by neural networks in the human brain, used to model and process data. Deep learning techniques are built upon these structures.
- Fuzzy Logic: An AI technique that operates with imprecise or uncertain data. It is commonly used in areas where uncertainty is prevalent in real-world applications.
- Natural Language Query (NLQ): A technology that enables users to ask questions in natural language and receive meaningful responses. Frequently used in database queries.
- Symbolic and Connectionist Approaches: Two fundamental approaches to information processing in AI. The symbolic approach is logic-based, while the connectionist approach relies on artificial neural networks.
There are two different modeling approaches in artificial intelligence: Generative Models and Discriminative Models. Both discriminative and generative models perform the same task by calculating the conditional probability of the target variable to discover patterns and relationships.
To explain the difference with an example, a discriminative model can be used to determine whether an image is of a cat or a dog. A generative model, on the other hand, can be used to create images similar to real ones. Examples of discriminative models in daily life include spam email filtering and identity verification through facial recognition.
As a result, discriminative models classify data by identifying patterns in training data and learning the distinguishing features that separate categories, while generative models can generate realistic data samples.
Generative AI Technology
This technology is designed to learn from existing data and generate new and original outputs. Generative AI systems learn from large datasets and provide creative solutions in various fields such as art, literature, music, and imaging.
Utilizing generative AI’s text, image, audio, and video generation capabilities to support academic studies and learning processes enables researchers and students to save time and work more efficiently.
Academic work requires in-depth research and effective writing processes. These processes can be time-consuming and demand intense focus and precision. Generative Artificial Intelligence (GAI) tools offer significant convenience for academics and students in various stages such as literature review, note-taking, summarization, and academic writing. With their text generation capabilities and analytical functions, these tools not only accelerate research processes but also enhance the linguistic, content, and structural quality of writing. Below are some GAI tools that can be useful in these processes, along with their features.
- ChatGPT (OpenAI): GPT-4o is widely recognized as one of the most advanced text generators. While some features of GPT-4o are available for free, full access requires a subscription.
- Gemini (Google): Also known as Bard, this system is a powerful large language model (LLM) that operates similarly to GPT. A limited version is available for free with a Google account.
- NotebookLM: NotebookLM is a generative AI tool developed by Google that helps users organize and understand their notes more efficiently. Designed for research and writing processes, this tool can establish connections between notes, generate summaries, and answer user queries. Additionally, it can convert academic papers into podcasts, allowing users to listen to required readings. Currently, it is available in beta for select user groups and can be accessed with a Google account.
- Grok: Developed by Elon Musk’s xAI, Grok is an AI model integrated with the X (Twitter) platform. It offers features such as real-time data analysis, trend identification, news summarization, and answering technical questions. For software developers, it provides coding assistance and debugging support, while in content production, it can generate blog posts, articles, and advertising texts. Additionally, it serves as a personal assistant, handling tasks such as scheduling, email responses, and meeting notes. It is also widely used in automating customer service and financial analysis, making it a powerful AI in social media and real-time data processing.
- SciSpace: SciSpace is a generative AI tool that analyzes academic papers, summarizes content, and helps with understanding scientific texts. It offers features like question-answering based on article content, text restructuring, and direct PDF analysis. Basic features are free, but advanced analytics require a paid plan.
- QuillBot: QuillBot is a writing assistant used for paraphrasing, summarizing, and grammar correction in academic writing. It enhances writing quality, improves sentence clarity, and helps summarize information from literature reviews. A free version is available, but a premium plan offers access to more comprehensive features.
- Co-Pilot (Microsoft): Based on GPT technology, this tool is integrated into Microsoft 365, with some features available for free for individual users.
- Claude (Anthropic): Claude 3 is an AI tool specialized in text and data processing. It stands out for its ethical design and secure use. Different Claude models offer flexibility for users who need either fast outputs or detailed analysis.
- Meta.ai (Meta): Integrated into Meta applications, this system is still under development and being tested with limited feedback. Its text generation capabilities have received mixed reviews.
- Perplexity (PerplexityAI): Designed for research and information retrieval, Perplexity is an effective tool for accessing real-time information on current events and news. It offers some features without requiring registration. Additionally, it includes a “Focus” mode that allows users to perform specialized searches, such as scanning academic papers and YouTube videos. Users can also switch between different AI models like Claude and GPT for enhanced flexibility.
Visualization is a crucial element that enhances the impact of academic and educational projects. Generative AI tools allow users to create complex and aesthetically pleasing images using only text descriptions. Image generation tools can be used in various fields, such as visualizing research findings, preparing educational materials, and producing unique content for creative projects. These tools serve as powerful assistants across multiple disciplines, from art and design to data visualization. Below are some notable generative AI tools for image creation and their functions.
- DALL-E: Version 3 makes it easier to transform ideas into visuals. It is integrated into OpenAI’s ChatGPT tool and may be available for free under certain plans.
- Adobe Firefly: Allows users to generate images from text or edit existing images. It is integrated into Photoshop and other Adobe Creative Cloud applications. Adobe states that its AI has not been trained on copyrighted images and that all generated visuals are marked as AI-generated.
- Image FX: A free tool that produces high-quality, realistic images.
- Microsoft Designer: Powered by DALL-E technology, this tool is free with a Microsoft account. It can be used to create graphics and social media content.
- Meta.ai: Integrated into Meta applications, this tool is still in the development phase for image generation. User feedback has been mixed.
- Midjourney: A paid AI tool that generates images from text prompts via the Discord platform. It provides highly detailed visuals.
- NightCafe: An AI-powered art creation platform that focuses on community-driven content. It offers a credit-earning system for free access.
- Craiyon: A basic AI tool that transforms text prompts into artwork. It provides a limited number of free images.
- Hotpot: Specialized in AI-generated artworks, portraits, corporate portraits, and avatar creation.
- Getty Images: Does not create AI-generated content but is used to license images from the Getty Images library.
- Recraft: A tool focused on image editing and adding text to visuals. It offers both free and paid plans.
Video content is one of the most effective ways to convey information and capture attention. Generative AI tools enable fast and creative video production processes for academic and educational projects. These tools help users create professional-looking videos using text descriptions, images, or raw footage. They can be used in a wide range of applications, from presenting research findings and developing educational materials to promotional videos and creative projects. Below are some notable AI tools for video creation and their features.
- DeepMotion: Allows users to create animations with AI-powered motion capture and real-time 3D body tracking. It is widely used in game development and digital art projects.
- Sora: Developed by OpenAI, Sora is a video creation tool that enables the production of professional-quality videos from text descriptions or raw content. Using advanced AI algorithms, it generates animations, voiceovers, transition effects, and scenes tailored to user-provided scripts. Sora can be used for educational videos, research visualization, promotional materials, and creative projects.
- Fliki: Provides realistic voiceovers and AI-powered video clip generation. It is used to quickly create videos from text.
- Lumen5: A user-friendly platform that converts text or blog posts into videos. It offers an intuitive video creation experience, similar to preparing a presentation.
- Synthesia: A popular AI video generation platform that allows users to create professional videos without a microphone, camera, actor, or studio. It is commonly used for educational videos and corporate presentations.
- HeyGen: Works with user-provided scripts or AI-generated text. It has the ability to create and translate video content in 175 languages, making it an ideal option for multilingual content production.
Generative AI tools serve as powerful assistants for academics and students in learning, research, and writing processes. However, making the most of these tools requires applying the right methods and strategies. Fully utilizing their potential not only enhances the quality of results but also saves time and effort. Below are some tips and strategies for effectively using generative AI tools.
Provide Clear and Detailed Instructions: AI tools operate based on the instructions given by users. Providing clear and specific guidelines leads to more accurate and meaningful results. For example, if you need a summary or analysis, specify the scope and focus of the output you expect.
- Define Your Goal and Stay Focused: Before using AI tools, clearly define your objective. Are you conducting a literature review or editing a text? Using tools with a clear purpose helps you avoid irrelevant results.
- Verify the Reliability of Sources: Instead of accepting the information provided by AI tools at face value, verify its reliability. Before using the data in academic papers and projects, ensure that it comes from credible and authoritative sources.
- Enhance Your Own Writing and Ideas: Use AI tools as a guide or assistant, rather than becoming completely dependent on their outputs. In academic writing, prioritize your own ideas and develop AI-generated content to reflect your unique perspective.
- Know the Strengths of Different Tools: Understand which AI tool is more effective for specific tasks and use them accordingly. For instance, ChatGPT is ideal for text generation, while DALL-E is more suitable for image creation.
- Be Open to Experimentation and Improvement: Generative AI tools are constantly evolving. Experiment with different methods and explore ways to enhance your efficiency and workflow.
- Use AI Strategically for Time Management: AI tools provide a significant advantage in time management. Utilize them to speed up your research process, structure reports, or analyze complex data sets efficiently.
- Adhere to Ethical Usage Principles: Always follow ethical guidelines when using AI tools. In academic writing, remember to cite sources and give proper credit when incorporating AI-generated content.
- Protect Your Data: Avoid sharing sensitive or confidential information when using AI tools. To ensure data security, always choose reliable and trusted platforms.
- Provide Regular Feedback and Optimize Results: AI tools learn and improve based on the input and feedback they receive. If you get incorrect or incomplete results, provide feedback to help refine the tool’s responses.
By applying these tips, you can maximize the benefits of generative AI tools and enhance your academic work efficiently. When used correctly, these tools can significantly accelerate learning and productivity processes.
As the use of artificial intelligence (AI) tools in academic processes becomes increasingly widespread, the ethical and responsible use of these technologies is of great importance. When students use AI tools for assignments, thesis writing, and source research, and when academics utilize them for literature reviews and academic writing, adherence to ethical guidelines ensures the preservation of academic integrity and the reliability of knowledge. This section will comprehensively examine the ethical considerations related to the use of AI tools in an academic context.
General Ethical Principles in AI Usage
Transparency and Responsibility: Users should be informed about the generation processes and outcomes of AI tools and should clearly disclose how these tools are used. For example, if summaries or recommendations from AI are used in a thesis, these contributions must be explicitly acknowledged.
Citation and Source Attribution: Information provided by AI tools must be properly cited, especially when directly quoted. Failure to do so may be considered a violation of academic ethics. Presenting AI-generated texts as one’s original work constitutes plagiarism.
Verifying Accuracy and Reliability: The content generated by AI tools may not always be accurate or reliable. Users should verify AI-generated information with independent sources and avoid using incorrect or misleading data.
Data Privacy and Security: When working with sensitive information, especially in thesis and research processes, careful attention should be given to data shared with AI tools. Only trusted platforms with strong privacy policies should be used to ensure data security.
AI Ethics for Students
Principle of Originality: Students should prioritize their own original contributions when using AI tools for assignments and theses. Presenting AI-generated texts as their own work is considered unethical.
Use for Educational Purposes: AI tools should be used to support learning. For example, they can help in understanding complex concepts or drafting ideas. However, rather than becoming dependent on AI-generated content, students should focus on developing critical thinking and analytical skills.
Avoiding Plagiarism Risks: AI-generated content should be checked for originality using a plagiarism detection tool. Additionally, proper referencing and citation practices should be followed when using AI-generated text in academic work.
AI Ethics for Researchers
Use in Literature Review: Academics can use AI tools to accelerate literature reviews and gain a broader perspective. However, instead of directly transferring AI-generated summaries into academic papers, these results should go through a critical evaluation process. Given the possibility of AI hallucinations, researchers should adopt a questioning approach when interpreting AI-generated insights.
Support in Academic Writing: AI tools can assist in academic writing processes by providing grammar checks, summaries, or writing suggestions. However, the scientific content and arguments of an article should be entirely developed by the author.
Scientific Integrity: Any sections written or edited using AI should be explicitly disclosed. This ensures compliance with ethical standards in thesis and article writing and upholds scientific integrity.
How to Cite Generative AI Tools?
APA (7th Edition)
APA Style provides guidelines for citing and referencing generative AI tools. According to APA, the organization or individual that developed the model should be considered the author.
Since the examples in the APA blog post were last updated in April 2023, some information may now be outdated. For instance, ChatGPT no longer provides versioned releases that can be easily referenced. Below are updated citation templates and examples aligned with APA format while accounting for ongoing changes in generative AI tools.
In-Text Citation
Template:
(Developer of AI model, Year of version used)
Examples:
(OpenAI, 2024)
(Black Technology LTD, 2024)
Reference List Entry
Template:
Developer of AI model. (Year of version). Model name (Version number) [Model type or description]. Access date, URL.
Examples:
OpenAI. (2024). ChatGPT 4 [Large language model]. February 26, 2024, https://chat.openai.com.
Black Technology LTD. (2024). Stable Diffusion (Online version) [Image generator]. February 28, 2024, https://stablediffusionweb.com.
Note: ChatGPT has two main versions: ChatGPT 3.5 (free) and ChatGPT 4 (subscription-based). The version used will be displayed at the top of the chat interface.
Chicago (17th Edition)
Chicago Style provides two citation systems for referencing AI models: Notes and Bibliography and Author-Date System. According to Chicago, AI models do not need to be listed in a bibliography but should be cited in footnotes or in-text citations.
Footnote or Endnote (Notes and Bibliography System)
Template:
Note number. AI model name, Description of generated content, Developer, Date generated.
Examples:
- ChatGPT, response to “Explain methods for making pizza dough with common household ingredients,” OpenAI, February 26, 2024.
- Stable Diffusion, image generated in response to “Surrealist-style pepperoni pizza,” Black Technology LTD, February 28, 2024.
In-Text Citation (Author-Date System)
Examples:
(ChatGPT 4, February 26, 2024)
(Stable Diffusion, February 28, 2024)
MLA (9th Edition)
MLA Style does not treat AI tools as authors. Instead, citations should omit the author element and include a general link to the AI tool.
In-Text Citation
MLA guidelines suggest using a shortened version of the “source title” as needed. For generative AI, the title typically describes the generated content.
Examples:
(“Explain symbolism”)
(“Green light”)
Works Cited Entry
Template:
“Prompt title.” AI Model Name, Version, Developer, Date generated, URL.
Examples:
“Explain the symbolism of the green light in The Great Gatsby.” ChatGPT 4, OpenAI, February 26, 2024, https://chat.openai.com.
“Green light in The Great Gatsby in a futuristic style.” Stable Diffusion, Online version, Black Technology LTD, February 28, 2024, https://stablediffusionweb.com.