AI

5 Conversations to Use with AI* to Reflect on Hypothesis Social Annotations: What the “Conversations” are asking students to do: Conversation Central Question Scholarly Habit #1 Mapping Our Thinking What patterns do we notice? Observation #2 Finding the Missing Voices What perspecti...

AI, Learning, and Consent Discussion Forum Purpose Generative AI is changing how people learn, write, solve problems, and communicate. As a result, colleges, workplaces, and instructors are developing different expectations for how AI should and should not be used. Preparation Please read the Min...

HTML for “The Easy Button” Discussion Forum: Janet Mitchell Lambert Instructions: Copy the entire HTML below the line and paste it into Canvas when it is in editing mode and HTML mode. Make sure to turn it back to the Rich Content Editor before saving. When you switch to HTML, there is a spot ...

The “Easy Button”: what gets skipped? Students may seek relief from time pressure. We can teach a better decision. When AI does the target work It supplies the interpretation, claim, and analysis before the student attempts them. The student saves time but loses an opportunity to think, practice,...

Guiding Questions for Online Faculty for Handling AI and Thinking: What thinking does this assignment require? Identify the analysis, interpretation, reasoning, or rhetorical choices students must make. Where might AI let students skip that thinking? Identify the moment when a generated answer co...

In an online first-year English course, Dr. Carleigh Brady redesigned assessment to create multiple checkpoints that helped students engage with course readings, demonstrate their thinking—including orally—and receive feedback throughout the writing process. The redesign made learning more visible while adapting assessment to the realities of generative AI.

And most employers prefer to hire workers with a college degree, even if the job doesn’t require one.

In their fight against AI, professors are monitoring their students in ways previously unimaginable.

When prompts to draft work emails and job applications contain language commonly used by women, AI chatbots including ChatGPT offer less sophisticated responses compared to office correspondence requested with language associated with men, new Johns Hopkins University research finds.

Ethical AI use is something we teach—not merely something we police. Choose one or two areas below and add a concrete idea for something faculty can do, model, teach, or build into an assignment to help students make ethical choices when using AI. Think beyond adding another rule to your syllabus. What could we do before a problem occurs to help students understand what responsible AI use actually looks like? Your idea might be a brief activity, an assignment design choice, a conversation with students, an example you could show, a reflection question, or something you could model. Be practical: What is something an instructor could actually do in a college course?

Skip the “Easy Button” Why???? Don't outsource the part that makes you smarter. AI can generate an answer in seconds. But when you wrestle with a question, test an idea, and figure something out, you're building skills that belong to you. Your perspective is something AI doesn't have. Your experi...

Office correspondence is weaker when requested with language commonly used by women

Welcome to our Responsible AI series, created by the Digital Scholarship and Data Services department as part of its ongoing commitment to promoting ethical and responsible AI practices for the Hopkins community. AI, or Artificial Intelligence, is technology developed with the goal of performing tasks typically requiring human intelligence, such as recognizing patterns, making decisions, and […]

Welcome back to our Responsible AI series, created by the Digital Scholarship and Data Services department as part of its ongoing commitment to promoting ethical and responsible AI practices for the Hopkins community. Artificial intelligence (AI) is becoming part of the everyday research and learning environment. From tools that summarize articles to systems that generate […]

Welcome back to our Responsible AI series, created by the Digital Scholarship and Data Services department as part of its ongoing commitment to promoting ethical and responsible AI practices for the Hopkins community. “Fluent but Not Factual” Chatbots are designed to generate language that seems as coherent as human communication. They function as what computer […]

A new study from Johns Hopkins finds that multilingual AI privileges dominant languages, deepening divides rather than democratizing access to information

Welcome back to our Responsible AI series, created by the Digital Scholarship and Data Services department as part of its ongoing commitment to promoting ethical and responsible AI practices for the Hopkins community. Using AI tools responsibly means being aware of their potential for inaccuracy and evaluating their output before integrating it into your research […]

AI category; not for display in any sidebars, just for connecting a collection of priority posts on the topic of AI

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Is this post AI? What textbook is that? Why are there bolded headers? Wow, that student is using the work of a scholar I came across in graduate school! Awesome or cheating? Grading online...

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Lisa Sperber, Sophia Minnillo, Joy Ok, Anna Mills, Open Practices for Wicked Problems, Writing on the Edge, Vol. 33, No. 2 (Fall 2025), pp. 21-34

Written assignments for large classes pose a far more significant challenge in the age of the GenAI revolution. Suggestions such as oral exams and formative assessments are not always feasible with many students in a class. Therefore, we conducted a study in South Africa and involved 280 Honors students to explore the usefulness of Turnitin's AI detector in conjunction with student self-reflection. Using a Mixed Methods Research (MMR) approach, we analysed data generated from the Turnitin AI reports, our grading rubrics, and qualitative student self-reflection. The findings show that incorporating self-reflection into assessments supports ethical GenAI use and improves the transparency lecturers need for decision-making. A declaration form allowed the students to be upfront about using Generative Artificial Intelligence tools. We found that students who can reflect on their learning relied less on generated content. However, students with high AI detected scores ( 20%) did not adequately reflect on how the tools supported their learning and could not give credible explanations of use. We contribute to the body of knowledge by providing students and academics with examples of responsibly handling AI-detected scores in large-class settings. We present a guided self-reflection and declaration with an AI detector to support students and help lecturers make decisions when grading. We also present a decision tree that lecturers and graders can use when evaluating AI use in assessments.

A resource to help students build safe, ethical, and effective habits when using Generative AI, offering a structured way to reflect on their current use, recognize where GenAI is supporting their learning, and identify when they are over-relying on the technology.

Resources for faculty

This paper examines the transformative role of Large Language Models (LLMs) in education and their potential as learning tools, despite their inherent risks and