Teaching and Learning in Response to Generative AI Tools
-Prototype Version
The Generative AI Landscape
The advent of generative artificial intelligence (GAI) holds significant potential to revolutionize the methods we
teach, learn, assess, and access education. Integrating generative AI into education has significant implications
for critical thinking, writing, and academic integrity. By utilizing generative AI tools, students can amplify
their critical thinking skills through exposure to varied perspectives and interactive discussions with
AI-generated content. Moreover, generative AI can assist in improving writing abilities by offering suggestions,
grammar corrections, and even generating draft content. Nonetheless, it is vital to maintain a balance between
utilizing AI tools for support and preserving the authenticity and originality of students' work to uphold the
principles of academic integrity.
This website serves as a comprehensive resource for teaching with generative AI. It offers guidance on
incorporating AI tools in courses by providing information on available products in the market, along with
resources for detecting AI-generated content. The website also outlines Fordham University's course policy on
generative AI usage, offering sample syllabi statements for different scenarios. Additionally, faculty members
can find assignment design ideas to effectively integrate generative AI into their teaching practices while
exploring methods to mitigate cheating and encourage meaningful learning. The website features a forum for
engaging discussions through invited talks, tutorial videos, and panel discussions. Supplementary resources,
FAQs, and references are provided to support faculty and students in understanding and utilizing generative AI
within an educational context. Overall, this website aims to provide comprehensive guidance, resources, and a
platform for educators to navigate the ethical use of generative AI, promote academic integrity, and enhance the
learning experience.
Guidance for Teaching and Mitigating Cheating/Non-Learning with GAI Tools
This section of the website presents a compilation of generative AI software and tools available in the market,
along with resources for detecting AI-generated content. It outlines Fordham University's course
policy regarding the use of generative AI, including sample statements that can be incorporated into syllabi for
different scenarios, such as no-AI, limited-AI, and full-AI usage. It also offers assignment ideas for effectively integrating
GAI into teaching practice, emphasizing the methods and techniques required to redesign assessment
approaches to mitigate cheating and non-learning.
Available generative AI tools and their capability
- Claude: from Anthropic. Marketed as being a more helpful and honest tool.
- GPT4: most recent version by OpenAI. Marketed as being more inventive and accurate.
- ChatGPT: most common version used by the public.
- ChatSonic: developed by the technology company Writesonic. Integrated with Google Search to create content
with real-time data. Generates visuals, voice commands, and more.
- AlphaCode: programming capabilities in Python, C++, and several other languages.
- GitHub Copilot: a code completion Artificial Intelligence tool.
- Bard: a chatbot and content generation tool developed by Google.
- Synthesia: a tool for creating videos. With little to no work, it rapidly generates and broadcasts videos of
professional quality.
- DALL-E2: OpenAI’s recent version for image and art generation.
- Copy.ai: creates variants of marketing texts for specific goals and target demographics.
- Murf.ai: an online tool that uses AI to generate high-quality voice-overs for videos, presentations, and
text-to-speech needs.
AI content detection tools
Guideline for sample statements to use in course syllabi
For a "No-AI" approach:
“Generative AI tools are not permitted in this course. Students [or learners] must rely on their own originality,
creativity and critical thinking skills to complete assignments and engage with course material.”
For a “Limited-AI” approach:
“Limited usage of generative AI tools may be allowed for specific assignments in this course, enabling exploration
of ideas, complex data analysis, and creative solution development, when explicitly permitted by the
instructor. When using these tools, it is mandatory to clearly indicate the sections of your work that were
generated using them for proper attribution and transparency, and indicate the prompts and software versions
that were used. It is critical to adhere to ethical standards by refraining from activities like plagiarism
or creating misleading content. Additional guidelines or restrictions will be provided for specific
assignments.”
For a “Full-AI” approach:
“This course allows the use of generative AI tools to facilitate exploration of innovative ideas, complex data
analysis, and creative solution development. Students must clearly indicate the sections of the work that were
generated using generative AI tools for proper attribution and transparency, and indicate the prompts and
software versions that were used. It is critical to adhere to ethical standards by refraining from activities
like plagiarism or creating misleading content. Additional guidelines or restrictions will be provided for
specific assignments.”
Sample assignments embracing GAI tools
- Conduct in-class discussions analyzing AI-generated writing to understand its strengths and limitations.
- Assign students to revise and edit AI-generated texts to elevate them to their own standards. Students will
submit both the original AI draft and their final version.
- Organize in-class presentations comparing and contrasting AI writing with human writing. Prompt students to
reflect on elements replicable by ChatGPT and aspects unique to human authors in their work.
- Explore refinement techniques by having students compose variations of the same prompt to fine-tune
AI-generated results.
- Scaffold engagement with AI tools by encouraging students to interact with AI, using it for brainstorming or
divergent thinking exercises.
Sample assignments deterrent of GAI tools
- Require oral presentation of coursework to assess students' understanding and communication skills
effectively, providing them with an opportunity to articulate their knowledge verbally.
- Employ interactive, in-class exercises to promote active learning and real-time application of concepts,
fostering a deeper understanding of the subject matter.
- Engage in case studies based on current events. Given ChatGPT's reliance on empirical data, this approach
effectively prevents students from obtaining answers solely from the software.
Sample new assessment methods
The advent of GAI necessitates new grading methods to mitigate the influence of AI writers.
For assignments that prohibit GAI tools, some new assessment ideas include:
- Assess the uniqueness of content using plagiarism detection tools or comparison with existing sources.
- Compare the quality and creativity of take-home assignments with in-class work, considering factors such as
coherence, style, and relevance.
- Evaluate content accuracy and relevance in addressing assignment objectives.
For assignments that allow GAI tools, some assessment ideas include:
- Prompt students to reflect on GAI's benefits and limitations, justifying their responses.
- Require students to submit the prompts used for GAI and assess their ability to effectively customize and
adapt AI-generated content to fit specific contexts or target audiences.
- Assess the practicality and usefulness of AI-generated content in real-world scenarios, such as marketing
materials or informational texts.
Resources and References
This section of the website will feature a forum inspired by similar initiatives organized by other universities
(as shown below). This forum could encompass a range of engaging formats, such as invited talks, tutorial videos,
or panel discussions. It will also provide other supporting resources for the fauclty and students.
Forum, tutorial videos, webinars, and workshops on generative AI
FAQs
- What is generative AI?
- How does generative AI work?
- What are the limitations of generative AI?
- Can I use generative AI for my teaching?
- What are the potential benefits of generative AI in education?
- What are the potential risks and challenges of using generative AI?
- How can generative AI be used responsibly in teaching and learning?
- How can academic integrity be maintained when using generative AI?
- What generative AI tools are available in the market?
- Are there tools to detect AI-generated content and why are they not so great?
- Are there guidelines for citing and attributing AI-generated content?
- What are the ethical considerations when using generative AI?
- How can I address potential biases in AI-generated content?
- Where can I learn about generative AI and other Generative AI tools?
Other resources and references for faculty and students
- Link to updated Academic Integrity Policy
- The Sentient Syllabus Project
- Bias and discrimination in generative AI
- Dwivedi et al., 2023. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities,
challenges and implications of generative conversational AI for research, practice and policy.
International Journal of Information Management, 71, 102642.
- Sun, L., Wei, M., Sun, Y., Suh, Y. J., Shen, L., & Yang, S. (2023). Smiling Women Pitching Down:
Auditing Representational and Presentational Gender Biases in Image Generative AI. arXiv preprint
arXiv:2305.10566.
- Ferrara, E. (2023). Should chatgpt be biased? challenges and risks of bias in large language models.
arXiv preprint arXiv:2304.03738.
- Srinivasan et al. (2021) Biases in Generative Art: A Causal Look from the Lens of Art History.
- AI Privacy concerns