Artificial Intelligence
in Teaching and Learning
Artificial intelligence (AI) is prolific in all we do: editorial assistants, chatbots, generative AI (GenAI), and more. In educational activities, faculty, instructional designers, students, and administration have an opportunity to build a shared understanding in potential uses and responses to AI’s presence. As AI continues its rapid evolution, we find ourselves embracing, avoiding, and questioning it from varied perspectives. Here we aim to unify some of these viewpoints and provide guidance in teaching and learning at the Bloomberg School of Public Health.
As an editorial assistant, many people have come to love the support offered by AI: accessibility checks, editorial suggestions (grammar, mechanics, style), etc. As a chatbot, which is finite in its knowledge and somewhat scripted in its response, we can appreciate its efficiency in our search for information – deciphering our communications and filtering any targeted responses by their presumed relevance. But GenAI – tools and platforms designed to learn, “think”, and perform like ChatGPT, Midjourney, Gemini, Claude, DALL-E, and more – brings about a more divisive reception. Should it be embraced or avoided? Should it be banned or incorporated? Should it be considered a potential collaborative partner or an aid to violating integrity or critical thinking? The only clear answer is that it should not be ignored.
The most promising outlook of AI in education might be that presented by Farrokhnia et al (2024):
“[R]eflect deeply on the issue and take advantage of [AI’s] opportunity for education while attempting to minimise its threats to education. In this scenario, we need to adjust curricula and include learning goals, learning tasks, and assessment approaches.”
The teaching and learning centers at Johns Hopkins University have collectively published Generative AI Tool Guidance that can be used by faculty and students. In addition, the Johns Hopkins Bloomberg School of Public Health offers the following information to our community.
FAQs
Does the School’s Code of Academic Ethics specifically mention AI?
There is nothing explicitly mentioning AI in our current Code. However, all students are made aware of the ethical implications of AI use during the School’s new student orientation and in a module (updated in January 2025) in the Academic and Research Ethics (ARE) course. Using AI without a reference, unless authorized by the course instructor, is already covered by prohibitions in the Ethics Code regarding cheating and plagiarism.
Can faculty use AI detection software to check student work?
AI detection tools are not permitted. The exception is for tools already licensed by the University, such as TurnItIn. Even with our licensed tools, the reported bias and false positives are strong arguments against using any of these tools. For detailed information, please review the University guidance specific to these tools’ limitations and alternatives.
What should faculty do if they suspect a violation of academic integrity that involves using AI?
Faculty should treat all suspected violations of academic integrity in the same manner. The recommended actions are:
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Provide room and opportunity for an open conversation with the students without projecting blame or evidence of your suspicion.
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Check your course and any activity policies for clarity and consistency.
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Contact Jon Vernick, Director of Academic Integrity for recommendations and any next steps. Do not take any action against the student before contacting Professor Vernick.
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Continue to value the students’ privacy and rights. Do not sleuth on your own.
Can faculty teams (instructors and teaching assistants) use AI to assist them in grading?
Yes, faculty can use AI to assist with grading as long as they do not violate FERPA Guidelines nor copyright restrictions. If using a tool not supported nor subscribed to by the School, refer to the selected platform’s policies (terms of use) to learn whether submitted content, including student work, is used by the technology to train itself.
Can faculty use AI to develop their learning activities?
Yes, faculty can use AI to assist them in developing their learning activities. In doing so, it is advised that they should be conscientious of what they generate. That is, faculty should evaluate AI output for potential bias and misinformation, plus be aware of the University Guidelines.
When should faculty cite their use of AI in their instructional activities, including communications?
Faculty should model their expectations of students in their teaching and research. Source citations, plagiarism, and copyright should be considered. (See Sheridan Library’s resources for Chicago, APA, MLA, and other AI citation styles.) Additionally, they should adhere to the University’s Intellectual Property Policy.
Is a course syllabus statement regarding student AI use required? Does it have to be reviewed or approved?
It is the faculty’s discretion to include a course syllabus statement specific to AI use. However, the BSPH Syllabus Guidance does recommend including such a section. Suggested example statements can be found in the CoursePlus Syllabus tool’s section: “Use of Artificial Intelligence Software."
When and where should faculty communicate expectations regarding AI use to students?
Faculty should clearly communicate expectations about AI use (even if it is already in the syllabus) in individual learning activity instructions and any assignment reminders. Additionally, it’s recommended that faculty clearly state that their AI policy is specific to only their course.
How can faculty support students’ understanding of ethical AI use?
Faculty should communicate the reasons they are allowing or restricting AI. Additionally recommended:
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Make reflection part of the activity.
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Provide guidance through examples and rubrics.
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Require process documentation and/or formative assessments.
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Provide guidance on how and when to cite AI if it is used. (See Sheridan Library’s resources for Chicago, APA, MLA, and other AI citation styles.)
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Provide scaffolded support by recommending appropriate tools, modeling prompts, and practicing evaluating results.
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Introduce or remind students of ethical considerations, including linking to terms of use for any recommended tool and the longer term environmental impacts.
How can faculty make their learning activities resistant to AI?
Faculty can engage their students, personalize their learning, strive to make activities distinctly human, and even consider using AI to design and revise their activities to make it more likely that students avoid the tendency to use AI in their learning. Here are some tips:
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Engagement: Start with transparency, providing salient objectives and communicating your rationale for avoiding AI. Build in opportunities for feedback and reflection in the activities.
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Personalized learning: Guide learner agency by having students tie in personal stories or applications in assignments. Additionally, allow students to learn and grow from missteps.
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Distinctly human activities:
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Build assignments that allow for iterative refinement, guided by instructor feedback and students’ self-regulated learning.
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Use formative, process-based activities that divide larger assignments into milestones (proposal, outline, draft, final submission). At each step, provide an assessment with constructive, meaningful feedback for the student to reflect on and accordingly adapt their progress.
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Request documentation of the students’ learning process throughout an activity. Examples might include logging activity including prompt revision or simply having a reflection journal. See Oregon State Bloom’s Taxonomy Revisited for more on “distinctly human” skills.
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The University’s guidance, How to Redesign the Assessment to Cope with AI Challenges, provides further information.
How can faculty incorporate AI in their learning activities?
Faculty can encourage and guide students in the activity, include fundamental criteria, enhance and innovate existing activities with AI incorporations, design new activities that embrace the possibilities afforded by AI, and consider using AI to design and revise their activities to make the most of the technology’s integration. For example:
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Encourage and guide: Speak from experience and communicate your rationale for incorporating AI, including your expectations of its use. Offer scaffolded support.
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Include fundamental criteria: Make what you expect clear in terms of AI’s role in the assignment and the transparency plus accountability of its use. Citations, prompts, and/or reflections might be considered as requirements.
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Enhance, innovate and design activities: Prompt AI to help you in your design; create new graphics; find demonstrations or sample case studies, or data sets; etc.
The University’s guidance, How to Use Generative AI Tools to Design Engaging Activities, provides further information.
What AI tools are currently offered in CoursePlus?
CoursePlus rolled out the AI Review Quiz Maker to faculty in the summer of 2024. This first tool creates quiz questions for faculty to consider using. The questions are based on lecture transcripts uploaded to an enterprise version of ChatGPT. A second CoursePlus AI tool, Draft Coach, gives targeted feedback to students about their writing. Draft Coach is available for student use in courses where faculty have opted-in to use the tool. More information can be found in the linked Blog posts.
Where can I learn more?
We have compiled a collection of resources in the AI in Education: Categorized Resources for Faculty document. Additionally, please refer to the University’s Generative AI Tool Guidance.
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Download these FAQs for Using AI in Teaching and Learning at BSPH.
References
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2023). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846
Johns Hopkins University. (n.d.) Generative AI guidance. https://teaching.jhu.edu/university-teaching-policies/generative-ai/
Oregon State University Ecampus. (2024). Bloom’s taxonomy revisited. https://ecampus.oregonstate.edu/faculty/artificial-intelligence-tools/blooms-taxonomy-revisited-v2-2024.pdf