Preparing to guide graduate students in the ethical use of generative AI tools for research
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Overview and Purpose
Although graduate students have received a lot of training specific to their field of study, they have not necessarily had much formal training in academic writing and general research methods. However, these skills are critical for a successful research career and completion of most graduate-level degree programs. Furthermore, training in research skills is complicated right now by uncertainty around the ethics of using generative AI tools for different tasks, including finding relevant literature, summarizing that literature, brainstorming and revising outlines of papers, and writing and revising drafts.
This mini-course aims to assist faculty in guiding graduate students towards appropriate and effective use of generative AI tools for research-related tasks, including conducting literature reviews, synthesizing findings from existing literature, refining ideas, and revising drafts. This course provides guidance to faculty on how to develop and discuss acceptable practices for generative AI use that are tailored to the student's discipline and readiness. However, generative AI use for data analysis, interpretation, and visualization of findings will not be covered in this mini-course.
Who should participate in this course?
This course is aimed at faculty in institutions of higher education who mentor graduate students on master's thesis and doctoral dissertation projects, as well as independent research. No prior knowledge of or experience with generative AI tools is required.
How will learning occur in this course?
This mini-course is delivered asynchronously online through the KNILT platform. Participants will need access to reliable high-speed Internet to access and engage with the course materials. However, no other resources or subscriptions will be required, as the course focuses on exploring freely available generative AI tools. Course resources and activities include articles to read, short videos to watch, self-assessments, collaborative discussions, journal reflections, and action steps as you develop a plan to guide your graduate students through the opportunities and challenges of generative AI tools for research.
What will you learn in this course?
By the end of this mini-course, you will be able to:
- Understand and articulate the advantages and disadvantages of students using generative AI for research in your discipline
- Evaluate generative AI tools that are available to assist with literature searches, literature synthesis, refining ideas, and revising outlines and drafts
- Analyze student readiness to use generative AI tools for research effectively
- Create a workplan template for guiding students' use of generative AI tools for research that can be tailored to each student's needs and readiness
Course Units
This mini-course includes the following units. Click the title of a unit to go to its page.
Unit 1: Making informed decisions about student use of generative AI tools for research
In this unit, you will have the opportunity to learn more about generative AI tools and how they are currently being used for research-related tasks. You will develop and articulate your own personal philosophy towards generative AI use in research based on existing guidelines relevant for your discipline and your own past experiences.
By the end of this unit, you will be able to:
- Describe the premise of generative AI and how generative AI tools are currently being used for research
- Discuss existing guidelines on generative AI use for research, from universities, professional societies, disciplinary scientific journals, and research organizations
- Identify the non-negotiable elements of ethical generative AI use for research in your discipline
- Articulate your own beliefs about the advantages and disadvantages of graduate students using generative AI for research-related tasks
In Unit 2, you will engage in an evaluation of existing generative AI tools, including deciding on criteria that are most important when reviewing the strengths and limitations of each tool for different research-related tasks.
By the end of this unit, you will be able to:
- Discuss different types of generative AI tools
- Describe elements of effective prompts for generative AI tools
- Identify relevant criteria for evaluating generative AI tools for different research-related tasks
- Evaluate generative AI tools for specific research-related tasks based on specified criteria
- Develop a plan for staying abreast of new generative AI technologies and advances in existing technologies
Unit 3: Analyzing student readiness to use generative AI tools effectively
In this unit, you will consider the characteristics and behaviors that signal a particular student has the prerequisite knowledge and skills to engage with generative AI tools to assist their research in appropriate and ethical ways.
By the end of this unit, you will be able to:
- Define key elements of student readiness to use generative AI tools effectively
- Develop a plan to analyze student readiness based on discussions, student completion of tasks, and student deliverables
- Rate student readiness based on your plan
In Unit 4, you will put all this information together to create a customizable template to guide your work with students, as they wrestle with incorporating and describing generative AI use in their research.
By the end of this unit, you will be able to:
- Discuss training vs. mentoring in the context of generative AI use for research
- Describe roles and responsibilities of students vs. instructors in the context of responsible and effective generative AI use for research-related tasks
- Outline steps that students will need to follow to define, incorporate, and describe their use of generative AI tools as part of their research proposal, workflow, and final products
- Develop a plan to evaluate and update your guidance for students