Table of Contents:
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Artificial Intelligence
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Pros, Cons, Examples in Higher Education
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Generative Artificial Intelligence (Gen AI)
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Pros, Cons, Examples in Higher Education
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Large Language Models
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Prompt Engineering
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AI/ GenAI Best Practices
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UT Dallas Position on AI
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AI/ Gen AI Tools at UT Dallas
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AI/ Gen AI Consultation
Artificial Intelligence (AI) refers to computer systems and technologies designed to perform tasks that typically require human intelligence. These tasks may include learning from data, recognizing patterns, understanding and generating language, solving problems, making predictions, and supporting decision-making. AI encompasses a broad range of technologies, including machine learning, natural language processing, computer vision, and generative AI.
Modern AI systems can analyze large amounts of information and produce outputs such as text, images, code, recommendations, and insights. However, AI does not possess human understanding, reasoning, or judgment. Its capabilities are limited by the data it is trained on, the algorithms used to develop it, and the context in which it operates. As a result, AI-generated output should be reviewed critically and used responsibly, particularly in academic and professional settings.
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Pros
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Cons
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Operational Efficiency - Automates repetitive tasks.
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Privacy Risks - Sensitive data may be exposed.
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Personalized Services - Tailors support to individual needs.
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Bias Concerns - May reflect training data biases.
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Student Engagement - Improves communication and support.
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Accuracy Issues - Results may be incorrect or incomplete.
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Admissions Support - Streamlines application processing.
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Limited Judgment - Cannot replace human decision-making.
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Alumni Engagement - Personalizes outreach and fund raising.
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Implementation Costs - Requires resources and maintenance.
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Resource Optimization - Improves planning and allocation.
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Transparency Challenges - Decisions may be difficult to explain.
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Campus Services - Enhances housing and support operations.
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Ethical & Regulatory Risks - Requires policy and compliance oversight
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Examples of AI Use in Higher Education
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Area
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Example
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Student Support
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AI tutoring chatbots answering coursework and campus questions.
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Academic Advising
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Personalized course recommendations based on degree requirements and goals.
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Teaching
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Automated grading for quizzes and objective assessments.
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Curriculum Development
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Generating practice questions, simulations, and learning materials.
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Research
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Literature review assistance, data analysis support, and summarization tools.
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Admissions & Administration
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Transcript parsing, document processing, workflow automation.
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Career Services
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Resume screening, interview prep, skill-gap analysis tools.
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Campus Operations
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Roommate matching, alumni engagement, and personalized communication systems.
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Generative Artificial Intelligence (Gen AI) refers to a subset of AI technologies capable of creating new content such as text, images, music, videos, and more, by learning patterns from existing data. Gen AI supports text generation and summarization by turning complex ideas into easy-to-understand content, visual content creation by generating images for design, art, and architecture, and enhanced learning experiences through interactive tools, quick feedback, and personalized support for different learning needs. Unlike traditional AI systems that analyze or categorize data, Gen AI generates novel outputs, providing significant opportunities for innovation and creativity. Popular Gen AI tools include OpenAI's ChatGPT, Google's Gemini, and Anthropic’s Claude.
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Research Assistance - Gen AI aids researchers in data analysis, simulation generation, and literature reviews, accelerating the research process and fostering innovation.
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Academic Integrity - The ease of generating content raises concerns about plagiarism and the authenticity of student work.
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Efficiency - AI can streamline tasks such as grading, feedback, and course management, allowing educators to focus more on teaching.
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Bias and Ethical Issues - Gen AI systems can inadvertently perpetuate biases present in their training data, leading to skewed or inaccurate outputs.
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Scalability - Enables institutions to manage larger groups of students effectively by scaling personalized and adaptive learning experiences
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Privacy and Security Risks - Requires careful management of student data to ensure privacy and security and not providing university, sensitive, research or personal data for training AI models unintentionally or intentionally.
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Examples of Gen AI Use in Higher Education
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Students
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Faculty
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Departments
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Personalized Learning – Tailored educational content based on learning style and pace.
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Automated Grading – AI-assisted grading to reduce workload and provide faster feedback.
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Personalized Alumni Relationship – Customized alumni communications for engagement and fundraising.
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Personal Course Selector – AI advisor for course planning, scheduling, and degree requirements.
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Course Improvement – Creation of quizzes, simulations, and instructional materials.
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Resume Filtering – Automated screening of applicants based on job requirements.
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Mock Interview / Funding Simulation – Practice interviews and funding scenarios for preparation.
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Plagiarism Detector – Detection of potential plagiarism in assignments and essays.
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Transcript Processing – Automated parsing and evaluation of admission transcripts.
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Large language models (LLMs) represent a class of artificial intelligence that focuses on natural language processing and providing relevant responses after analyzing bodies of text.
Existing models primarily use natural language processing and machine learning techniques to analyze large amounts of text data from various sources, including the greater internet, libraries, archive centers, and more. By doing so, they can uncover patterns and connections in bodies of text that may not be as easily accessible to a human researcher.
Due to the input token requirement and differences between processing layers of GPT models, strategies for crafting the best possible input for a model are required to effectively leverage the knowledge and capabilities of the model used. Each model has different input best practices, requirements and restrictions.
OIT recommends reviewing prompt engineering methods using the CRISPE method from the article: Article - How to Write Effective Prompts
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Data Privacy: Avoid entering university, personal, research, student, medical or sensitive information into public AI platforms unless confirmed they have a non-training agreement with us.
For comprehensive guidelines and further information, refer to the OIT’s Initial Guidelines for Gen AI.
By thoughtfully embracing Gen AI, UT Dallas aims to create an innovative and ethically responsible educational environment, equipping students and faculty to succeed in a rapidly evolving technological landscape.
ISO Guidelines
The UT Dallas Information Security Office (ISO) has published new AI Usage Guidelines for the responsible and effective use of artificial intelligence tools. Aimed at faculty and staff, the guidance offers practical recommendations for protecting UT Dallas data while taking full advantage of the advanced AI platforms that are licensed by the University.
The guidelines are not formal or standard policy. They suggest best practices with technology-specific configurations and tips to raise awareness, reduce risk, and encourage informed decision-making.
The University recognizes the transformative potential of AI and advocates for its responsible use. The Office of Information Technology (OIT) enables AI tools that meet security and privacy standards. Faculty and staff are encouraged to integrate these tools into teaching and research to enhance educational outcomes, creating an innovative and ethically responsible environment.
UT Dallas Generative AI Policy [UTDSP5017]
Purpose of the Policy:
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Establishes guidelines for ethical and responsible use of generative AI in academic work (coursework, research, theses, etc.).
Review the Policy: Generative AI Use in Academic Work [UTDSP5017]
The University of Texas at Dallas (UTD) offers a range of AI and Gen AI tools to support academic and administrative tasks. Here is an overview of the available tools, their accessibility, and other key details:
To review the AI and Generative AI tools available at UT Dallas, the Office of Information Technology (OIT) provides a comprehensive overview of supported platforms and resources for students, faculty, and staff. These tools include enterprise solutions such as Microsoft Copilot, CometAI, Grammarly, Adobe Firefly, and various cloud-based AI services (e.g., Azure, AWS & GCP), along with guidance on accessibility, use cases, and best practices for responsible AI usage across campus. For detailed information, please refer to the article: Artificial Intelligence (AI) and Generative AI (Gen AI) Tools Available at UTD
OIT provides research and administrative groups with access to private cloud services through Microsoft Azure, Amazon Web Services, and Google Cloud Platform, supported by Enterprise Agreements with Amazon Web Services, Microsoft, and Google that ensure educational pricing and privacy compliance; selecting the right platform depends on factors such as cost, availability, compatibility, existing projects, and in-house expertise. For detailed information, please refer to the article: Request Cloud Hosting.
Consult OIT: For personalized guidance on selecting and utilizing AI tools and Models effectively, follow these steps:
For latest information, please do visit OIT AI Studio for more information and updates.