Category: digital literacy

Teaching students to use AI: from digital competence to a learning outcome
by Concepción González García and Nina Pallarés Cerdà

Debates about generative AI in higher education often start from the same assumption: students need a certain level of digital competence before they can use AI productively. Those who already know how to search, filter and evaluate online information are seen as the ones most likely to benefit from tools such as ChatGPT, while others risk being left further behind.

Recent studies reinforce this view. Students with stronger digital skills in areas like problem‑solving and digital ethics tend to use generative AI more frequently (Caner‑Yıldırım, 2025). In parallel, work using frameworks such as DigComp has mostly focused on measuring gaps in students’ digital skills – often showing that perceived “digital natives” are less uniformly proficient than we might think (Lucas et al, 2022). What we know much less about is the reverse relationship: can carefully designed uses of AI actually develop students’ digital competences – and for whom?

In a recent article, we addressed this question empirically by analysing the impact of a generative AI intervention on university students’ digital competences (García & Pallarés, 2026). Students’ skills were assessed using the European DigComp 2.2 framework (Vuorikari et al, 2022).

Moving beyond static measures of digital competence

Research on students’ digital competences in higher education has expanded rapidly over the past decade. Yet much of this work still treats digital competence as a stable attribute that students bring with them into university, rather than as a dynamic and educable capability that can be shaped through instructional design. The consequence is a field dominated by one-off assessments, surveys and diagnostic tools that map students’ existing skills but tell us little about how those skills develop.

This predominant focus on measurement rather than development has produced a conceptual blind spot: we know far more about how digital competences predict students’ use of emerging technologies than about how educational uses of these technologies might enhance those competences in the first place.

Recent studies reinforce this asymmetry. Students with higher levels of digital competence are more likely to engage with generative AI tools and to display positive attitudes towards their use (Moravec et al, 2024; Saklaki & Gardikiotis, 2024). In this ‘competence-first’ model, digital competence appears as a precondition for productive engagement with AI. Yet this framing obscures a crucial pedagogical question: might AI, when intentionally embedded in learning activities, actually support the growth of the very competences it is presumed to require?

A second limitation compounds this problem: the absence of a standardised framework for analysing and comparing the effects of AI-based interventions on digital competence development. Although DigComp is widely used for diagnostic purposes, few studies employ it systematically to evaluate learning gains or to map changes across specific competence areas. As a result, evidence from different interventions remains fragmented, making it difficult to identify which aspects of digital competence are most responsive to AI-mediated learning.

There is, nevertheless, emerging evidence that AI can do more than simply ‘consume’ digital competence. Studies by Dalgıç et al (2024) and Naamati-Schneider & Alt (2024) suggest that integrating tools such as ChatGPT into structured learning tasks can stimulate information search, analytical reasoning and critical evaluation—provided that students are guided to question and verify AI outputs rather than accept them uncritically. Yet these contributions remain exploratory. We still lack experimental or quasi-experimental evidence that links AI-based instructional designs to measurable improvements in specific DigComp areas, and we know little about whether such benefits accrue equally to all students or disproportionately to those who already possess stronger digital skills.

This gap matters. If digital competences are conceived as malleable rather than fixed, then AI is not merely a technology that demands certain skills but a pedagogical tool through which those skills can be cultivated. This reframing shifts the centre of the debate: away from asking whether students are ready for AI, and towards asking whether our teaching practices are ready to use AI in ways that promote competence development and reduce inequalities in learning.

Our study: teaching students to work with AI, not around it

We designed a randomised controlled trial with 169 undergraduate students enrolled in a Microeconomics course. Students were allocated by class group to either a treatment or a control condition. All students followed the same curriculum and completed the same online quizzes through the institutional virtual campus.

The crucial difference lay in how generative AI was integrated:
- In the treatment condition, students received an initial workshop on using large language models strategically. They practised:
- contextualising questions
- breaking problems into steps
- iteratively refining prompts
- and checking their own solutions before turning to the AI.
- Throughout the course, their online self-assessments included adaptive feedback: instead of simply marking answers as right or wrong, the system offered hints, step-by-step prompts and suggestions on how to use AI tools as a thinking partner.
- In the control condition, students completed the same quizzes with standard right/wrong feedback, and no training or guidance on AI.
Importantly, the intervention did not encourage students to outsource solutions to AI. Rather, it framed AI as an interactive study partner to support self-explanation, comparison of strategies and self-regulation in problem solving.

We administered pre- and post-course questionnaires aligned with DigComp 2.2, focusing on five competences: information and data literacy, communication and collaboration, safety, and two aspects of problem solving (functional use of digital tools and metacognitive self-regulation). Using a difference-in-differences model with individual fixed effects, we estimated how the probability of reporting the highest level of each competence changed over time for the treatment group relative to the control group.

What changed when AI was taught and used in this way?

At the overall sample level, we found statistically significant improvements in three areas:
- Information and data literacy – students in the AI-training condition were around 15 percentage points more likely to report the highest level of competence in identifying information needs and carrying out effective digital searches.
- Problem solving – functional dimension – the probability of reporting the top level in using digital tools (including AI) to solve tasks increased by about 24 percentage points.
- Problem solving – metacognitive dimension – a similar 24-point gain emerged for recognising what aspects of one’s digital competences need to be updated or improved.
In other words, the AI-integrated teaching design was associated not only with better use of digital tools, but also with stronger awareness of digital strengths and weaknesses – a key ingredient of autonomous learning. Communication and safety competences also showed positive but smaller and more uncertain effects. Here, the pattern becomes clearer when we look at who benefited most.

A compensatory effect: AI as a potential leveller, not just an amplifier

When we distinguished students by their initial level of digital competence, a pattern emerged. For those starting below the median, the intervention produced large and significant gains in all five competences, with improvements between 18 and 38 percentage points depending on the area. For students starting above the median, effects were smaller and, in some cases, non-significant.

This suggests a compensatory effect: students who began the course with weaker digital competences benefited the most from the AI-based teaching design. Rather than widening the digital gap, guided use of AI acted as a levelling mechanism, bringing lower-competence students closer to their more digitally confident peers.

Conceptually, this challenges an implicit assumption in much of the literature – namely, that generative AI will primarily enhance the learning of already advantaged students, because they are the ones with the skills and confidence to exploit it. Our findings show that, when AI is embedded within intentional pedagogy, explicit training and structured feedback, the opposite can happen: those who started with fewer resources can gain the most.

From ‘allow or ban’ to ‘how do we teach with AI?’

For higher education policy and practice, the implications are twofold.

First, we need to stop thinking of digital competence purely as a prerequisite for using AI. Under the right design conditions, AI can be a pedagogical resource to build those competences, especially in information literacy, problem solving and metacognitive self-regulation. That means integrating AI into curricula not as an add-on, but as part of how we teach students to plan, monitor and evaluate their learning.

Second, our results suggest that universities concerned with equity and digital inclusion should focus less on whether students have access to AI tools (many already do) and more on who receives support to learn how to use them well. Providing structured opportunities to practise prompting, to critique AI outputs and to reflect on one’s own digital skills may be particularly valuable for students who enter university with lower levels of digital confidence.

This does not resolve all the ethical and practical concerns around generative AI – far from it. But it shifts the conversation. Instead of treating AI as an external threat to academic integrity that must be tightly controlled, we can start to ask:
- How can we design tasks where the added value lies in asking good questions, justifying decisions and evaluating evidence, rather than in producing a single ‘correct’ answer?
- How can we support students to see AI not as a shortcut to avoid thinking, but as a tool to think better and know themselves better as learners?
- Under what conditions does AI genuinely help to close digital competence gaps, and when might it risk opening new ones?
Answering these questions will require further longitudinal and multi-institutional research, including replication studies and objective performance measures alongside self-reports. Yet the evidence we present offers a cautiously optimistic message: teaching students how to use AI can be part of a strategy to strengthen digital competences and reduce inequalities in higher education, rather than merely another driver of stratification.

Concepción González García is Assistant Professor of Economics at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain, and holds a PhD in Economics from the University of Alicante. Her research interests include macroeconomics, particularly fiscal policy, and education.

Nina Pallarés is Assistant Professor of Economics and Academic Coordinator of the Master’s in Management of Sports Entities at the Faculty of Economics and Business, Catholic University of Murcia (UCAM), Spain. Her research focuses on applied econometrics, with particular emphasis on health, labour, education, and family economics.

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Categories: Libraries, Publishing, and Information Technology, Policy and HE, Student Experience | Tags: AI, AI literacy, AI policy, Digital inclusion, digital literacy, evolution in teaching practice, GenAI, generative AI, inequality, Teaching and Learning in HE | Permalink.

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January 30, 2026
Why critical data literacy belongs in every K–12 classroom

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An unexpected group of presenters–11th graders from Whitney M. Young Magnet High School in Chicago–made a splash at this year’s ACM Conference on Fairness, Accountability, and Transparency (FAccT). These students captivated seasoned researchers and professionals with their insights on how school environments shape students’ views of AI. “I wanted our project to serve as a window into the eyes of high school students,” said Autumn Moon, one of the student researchers.

What enabled these students to contribute meaningfully to a conference dominated by PhDs and industry veterans was their critical data literacy–the ability to understand, question, and evaluate the ethics of complex systems like AI using data. They developed these skills through their school’s Data is Power program.

Launched last year, Data is Power is a collaboration among K-12 educators, AI ethics researchers, and the Young Data Scientists League. The program includes four pilot modules that are aligned to K-12 standards and cover underexplored but essential topics in AI ethics, including labor and environmental impacts. The goal is to teach AI ethics by focusing on community-relevant topics chosen by our educators with input from students, all while fostering critical data literacy. For example, Autumn’s class in Chicago used AI ethics as a lens to help students distinguish between evidence-based research and AI propaganda. Students in Phoenix explored how conversational AI affects different neighborhoods in their city.

Why does the Data is Power program focus on critical data literacy? In my former role leading a diverse AI team at Amazon, I saw that technical skills alone weren’t enough. We needed people who could navigate cultural nuance, question assumptions, and collaborate across disciplines. Some of the most technically proficient candidates struggled to apply their knowledge to real-world problems. In contrast, team members trained in critical data literacy–those who understood both the math and the societal context of the models–were better equipped to build responsible, practical tools. They also knew when not to build something.

As AI becomes more embedded in our lives, and many students feel anxious about AI supplanting their job prospects, critical data literacy is a skill that is not just future-proof–it is future-necessary. Students (and all of us) need the ability to grapple with and think critically about AI and data in their lives and careers, no matter what they choose to pursue. As Milton Johnson, a physics and engineering teacher at Bioscience High School in Phoenix, told me: “AI is going to be one of those things where, as a society, we have a responsibility to make sure everyone has access in multiple ways.”

Critical data literacy is as much about the humanities as it is about STEM. “AI is not just for computer scientists,” said Karren Boatner, who taught Autumn in her English literature class at Whitney M. Young Magnet High School. For Karren, who hadn’t considered herself a “math person” previously, one of the most surprising parts of the program was how much she and her students enjoyed a game-based module that used middle school math to explain how AI “learns.” Connecting math and literature to culturally relevant, real-world issues helps students see both subjects in a new light.

As AI continues to reshape our world, schools must rethink how to teach about it. Critical data literacy helps students see the relevance of what they’re learning, empowering them to ask better questions and make more informed decisions. It also helps educators connect classroom content to students’ lived experiences.

If education leaders want to prepare students for the future–not just as workers, but as informed citizens–they must invest in critical data literacy now. As Angela Nguyen, one of our undergraduate scholars from Stanford, said in her Data is Power talk: “Data is power–especially youth and data. All of us, whether qualitative or quantitative, can be great collectors of meaningful data that helps educate our own communities.”

Evan Shieh, Young Data Scientists League

Evan Shieh is the Executive Director of the Young Data Scientists League.

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October 2, 2025
Before you click on that incredible deal…

Scammers are everywhere on the internet, masquerading to obtain your personal information. Many social media users or website creators pose as government entities or other authorities to offer you things that seem too good to be true or use scare tactics, like fake warnings about things like late fines or missed court dates, to prompt online users into sharing personal information.

In an era of misinformation, how do we know when a website is real?

One way is to research a website’s domain. A domain name is the part of a website address preceded by .com, .net or other popular suffixes. It’s essentially just the base website name without the “https://” and “www.”

“Measuring a website’s credibility might take time,” said Jordan Lyle, a senior reporter for Snopes.com. “Young journalists should know their stuff when it comes to domains and redirects.”

Snopes.com is one of the internet’s oldest fact-checking websites. He has more than 25 years of experience in managing websites and knows how to determine whether a site is legit.

Investigating internet sites

Alex Kasprak, a former investigative journalist at Snopes.com, has conducted numerous investigations using information gleaned from Domain Name Server (DNS) registers. DNS registers contain information about a particular website, its URL and IP address — a unique number on every tech device you might use.

With the information he found, Kasprak has been able to uncover unreported connections between news websites and their funders and between scammers and their beneficiaries.

“DNS tools are a great first step into any investigation that involves the identity of people behind websites or possible undisclosed connections between them,” Kasprak said.

Taking the expertise from these two investigative reporters, News Decoder has compiled the toolkit below to help perform a credible and comprehensive examination for publishing.

Are there red flags?

Scam websites have certain red flags. They might lack legal documentation, for example, including terms of service and privacy policies.

Another sign is sloppiness and mistakes. Try skimming through various pages on the site to look for typos, glaringly incorrect information, vague contact information, skewed formatting and other things that seem unprofessional.

Lyle said that a website that promotes a specific giveaway might lack any biographical or contact information about the people promoting the product or offer.

“Sometimes, scammers will include a mailing address that, upon searching for it, turns out to be a fulfillment center or a business that allows LLCs to anonymously register with that business’ physical office as a virtual address, shielding the scam’s operators from being identified,” Lyle said.

Conduct a website domain search.

Kasprak said that the Internet Corporation for Assigned Names and Numbers (ICANN) operates as a phonebook for the internet.

“In this analogy, the phone numbers are Internet Protocol (IP) addresses — a string of numbers formatted like 0.0.0.0 — and the ‘names’ are the actual domain names [e.g. news-decoder.com] to which those IP addresses are associated,” Kasprak said. “Like a human with a phone, domain names can change IP addresses several times.”

The first step for tracing the origins of a website involves what’s known as a “WHOIS” search — a specific type of domain search listing information about the creation of a domain.

WHOIS is a public database that lists several contact numbers, names or organisations associated with a given IP address or domain name. Many people these days use services that allow one to register a website anonymously, making the results have limited value. Older records, or those from some non-Western nations, often include actual names or corporate contacts, explained Kasprak.

A WHOIS search, which can be conducted at godaddy.com/whois, queries the public WHOIS database.

Lyle said he often looks at the date a person officially purchased and registered a domain name.. “For example, in the case of researching potential scams, if a domain name was recently registered, that’s a red flag indicating the website might be untrustworthy and could confirm the potential scam as legitimate,” he said.

Look at the site history.

Another great tool to pair with “WHOIS” searches is the Internet Archive’s Wayback Machine. When performing a “WHOIS” search on godaddy.com/whois, check to see when the domain was created. That year should match the Wayback Machine’s records of creation date, as well as show if the website had other owners with completely different websites.

“Also, know that the domain information listed in a WHOIS search might be the most recent data, but not the original data,” Lyle said. “Check the Wayback Machine to see if the website existed long ago in another form.”

Scammers might also create fake domains to pretend to be a legitimate business, adjusting the URL link slightly to trick users. A fake Home Depot ad on Facebook, for example, didn’t lead to homedepot.com when clicked through, but instead to “h0medepott.com”; an “o” was changed to a zero and a second “t” was added to the end of the URL.

“Scammers have created fake domains almost matching the genuine business domain for banks, as well as for USPS, for example,” Lyle said. “Sometimes, scammers won’t even bother to create similar domain names and instead simply rely on people not looking at the URL.”

Some scammers go so far as to copy the web design of a company — logo and all — to trick consumers. These types of scam websites often offer giveaways that seem too good to be true, such as free money, super inexpensive offers for goods or services or non-existent programs for student loan forgiveness.

“Of course, the biggest red flag would be an offer that seems too good to be true,” Lyle said. “If an offer seems too good to be true, it probably is. And I will go a step further: In 2025, if an offer seems too good to be true, it is. Avoid it.”

For journalists all this should becoming standard practice when using information off the internet in news stories.

“Basically, you want to make sure you did everything you could with your research before publishing your article,” Lyle said. “And that you attempted to go above and beyond expectations other publishers might have for their articles’ comprehensive credibility.”

Questions to consider:

1. What are some common red flags that a website might be fake or trying to scam you?

2. What is a DNS register and how is it useful to identify a potential scam?

3. If a friend sent you an unknown link, what steps would you take before clicking? How would you explain your choice to click or not?

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September 30, 2025
Weaving digital citizenship into edtech innovation

Key points:

What happens when over 100 passionate educators converge in Chicago to celebrate two decades of educational innovation? A few weeks ago, I had the thrilling opportunity to immerse myself in the 20th anniversary of the Discovery Educator Network (the DEN), a week-long journey that reignited my passion for transforming classrooms.

From sunrise to past sunset, my days at Loyola University were a whirlwind of learning, laughter, and relentless exploration. Living the dorm life, forging new connections, and rekindling old friendships, we collectively dove deep into the future of learning, creating experiences that went far beyond the typical professional development.

As an inaugural DEN member, the professional learning community supported by Discovery Education, I was incredibly excited to return 20 years after its founding to guide a small group of educators through the bountiful innovations of the DEN Summer Institute (DENSI). Think scavenger hunts, enlightening workshops, and collaborative creations–every moment was packed with cutting-edge ideas and practical strategies for weaving technology seamlessly into our teaching, ensuring our students are truly future-ready.

During my time at DENSI, I learned a lot of new tips and tricks that I will pass on to the educators I collaborate with. From AI’s potential to the various new ways to work together online, participants in this unique event learned a number of ways to weave digital citizenship into edtech innovation. I’ve narrowed them down to five core concepts; each a powerful step toward building future-ready classrooms and fostering truly responsible digital citizens.

Use of artificial intelligence

Technology integration: When modeling responsible AI use, key technology tools could include generative platforms like Gemini, NotebookLM, Magic School AI, and Brisk, acting as ‘thought partners’ for brainstorming, summarizing, and drafting. Integration also covers AI grammar/spell-checkers, data visualization tools, and feedback tools for refining writing, presenting information, and self-assessment, enhancing digital content interaction and production.

Learning & application: Teaching students to ethically use AI is key. This involves modeling critical evaluation of AI content for bias and inaccuracies. For instance, providing students with an AI summary of a historical event to fact-check with credible sources. Students learn to apply AI as a thought partner, boosting creativity and collaboration, not replacing their own thinking. Fact-checking and integrating their unique voices are essential. An English class could use AI to brainstorm plot ideas, but students develop characters and write the narrative. Application includes using AI for writing refinement and data exploration, fostering understanding of AI’s academic capabilities and limitations.

Connection to digital citizenship: This example predominantly connects to digital citizenship. Teaching responsible AI use promotes intellectual honesty and information literacy. Students can grasp ethical considerations like plagiarism and proper attribution. The “red, yellow, green” stoplight method provides a framework for AI use, teaching students when to use AI as a collaborator, editor, or thought partner–or not at all.This approach cultivates critical thinking and empowers students to navigate the digital landscape with integrity, preparing them as responsible digital citizens understanding AI’s implications.

Digital communication

Technology integration: Creating digital communication norms should focus on clarity with visuals like infographics, screenshots, and video clips. Canva is a key tool for a visual “Digital Communication Agreement” defining online interaction expectations. Include student voice by the integration and use of pictures and graphics to illustrate behaviors and potentially collaborative presentation / polling tools for student involvement in norm-setting.

Learning & application: Establishing clear online interaction norms is the focus of digital communication. Applying clear principles teaches the importance of visuals and setting communication goals. Creating a visual “Digital Communication Agreement” with Canva is a practical application where students define respectful online language and netiquette. An elementary class might design a virtual classroom rules poster, showing chat emojis and explaining “think before you post.” Using screenshots and “SMART goals” for online discussions reinforces learning, teaching constructive feedback and respectful debate. In a middle school science discussion board, the teacher could model a respectful response like “I understand your point, but I’m wondering if…” This helps students apply effective digital communication principles.

Connection to digital citizenship: This example fosters respectful communication, empathy, and understanding of online social norms. By creating and adhering to a “Digital Communication Agreement,” students develop responsibility for online interactions. Emphasizing respectful language and netiquette cultivates empathy and awareness of their words’ impact. This prepares them as considerate digital citizens, contributing positively to inclusive online communities.

Content curation

Technology integration: For understanding digital footprints, one primary tool is Google Drive when used as a digital folder to curate students’ content. The “Tech Toolbox” concept implies interaction with various digital platforms where online presence exists. Use of many tools to curate content allows students to leave traces on a range of technologies forming their collective digital footprint.

Learning & application: This centers on educating students about their online presence’s permanence and nature. Teaching them to curate digital content in a structured way, like using a Google Drive folder, is key. A student could create a “Digital Portfolio” in Google Drive with online projects, proud social media posts, and reflections on their public identity. By collecting and reviewing online artifacts, students visualize their current “digital footprint.” The classroom “listening tour” encourages critical self-reflection, prompting students to think about why they share online and how to be intentional about their online identity. This might involve students reviewing anonymized social media profiles, discussing the impression given to future employers.

Connection to digital citizenship: This example cultivates awareness of online permanence, privacy, responsible self-presentation, and reputation management. Understanding lasting digital traces empowers students to make informed decisions. The reflection process encourages the consideration of their footprint’s impact, fostering ownership and accountability for online behavior. This helps them become mindful, capable digital citizens.

Promoting media literacy

Technology integration: One way to promote media literacy is by using “Paperslides” for engaging content creation, leveraging cameras and simple video recording. This concept gained popularity at the beginning of the DEN through Dr. Lodge McCammon. Dr. Lodge’s popular 1-Take Paperslide Video strategy is to “hit record, present your material, then hit stop, and your product is done” style of video creation is something that anyone can start using tomorrow. Integration uses real-life examples (likely digital media) to share a variety of topics for any audience. Additionally, to apply “Pay Full Attention” in a digital context implies online viewing platforms and communication tools for modeling digital eye contact and verbal cues.

Learning & application: Integrating critical media consumption with engaging content creation is the focus. Students learn to leverage “Paperslides” or another video creation method to explain topics or present research, moving beyond passive consumption. For a history project, students could create “Paperslides” explaining World War II causes, sourcing information and depicting events. Learning involves using real-life examples to discern credible online sources, understanding misinformation and bias. A lesson might show a satirical news article, guiding students to verify sources and claims through their storyboard portion. Applying “Pay Full Attention” teaches active, critical viewing, minimizing distractions. During a class viewing of an educational video, students could pause to discuss presenter credentials or unsupported claims, mimicking active listening. This fosters practical media literacy in creating and consuming digital content.

Connection to digital citizenship: This example enhances media literacy, critical online information evaluation, and understanding persuasive techniques. Learning to create and critically consume content makes students informed, responsible digital participants. They identify and question sources, essential for navigating a digital information-saturated world. This empowers them as discerning digital citizens, contributing thoughtfully to online content.

Collaborative problem-solving

Technology integration: For practicing digital empathy and support, key tools are collaborative online documents like Google Docs and Google Slides. Integration extends to online discussion forums (Google Classroom, Flip) for empathetic dialogue, and project management tools (Trello, Asana) for transparent organization.

Learning & application: This focuses on developing effective collaborative skills and empathetic communication in digital spaces. Students learn to work together on shared documents, applying a “Co-Teacher or Model Lessons” approach where they “co-teach” each other new tools or concepts. In a group science experiment, students might use a shared Google Doc to plan methodology, with one “co-teaching” data table insertion from Google Sheets. They practice constructive feedback and model active listening in digital settings, using chat for clarification or emojis for feelings. The “red, yellow, green” policy provides a clear framework for online group work, teaching when to seek help, proceed cautiously, or move forward confidently. For a research project, “red” means needing a group huddle, “yellow” is proceeding with caution, and “green” is ready for review.

Connection to digital citizenship: This example is central to digital citizenship, developing empathy, respectful collaboration, and responsible problem-solving in digital environments. Structured online group work teaches how to navigate disagreements and offers supportive feedback. Emphasis on active listening and empathetic responses helps internalize civility, preparing students as considerate digital citizens contributing positively to online communities.

These examples offer a powerful roadmap for cultivating essential digital citizenship skills and preparing all learners to be future-ready. The collective impact of thoughtfully utilizing these or similar approaches , or even grab and go resources from programs such as Discovery Education’s Digital Citizenship Initiative, can provide the foundation for a strong academic and empathetic school year, empowering educators and students alike to navigate the digital world with confidence, integrity, and a deep understanding of their role as responsible digital citizens.

In addition, this event reminded me of the power of professional learning communities. Every educator needs and deserves a supportive community that will share ideas, push their thinking, and support their professional development. One of my long-standing communities is the Discovery Educator Network (which is currently accepting applications for membership).

Dr. Stephanie J. Madlinger, Discovery Educator Network (DEN)

Dr. Stephanie J. Madlinger, Ed.D., is an experienced educator and inaugural member of the Discovery Educator Network (DEN). With a deep passion for integrating technology into learning environments, she focuses on innovative teaching strategies and the responsible use of technological tools to enhance student engagement and promote digital citizenship. Her work emphasizes preparing students to be future-ready learners who can navigate the digital world with confidence and integrity. As a seasoned educator, professor, and staff developer, Stephanie is dedicated to fostering innovative learning environments in both traditional and online settings. Dr. Madlinger has facilitated and taught thousands of learners in traditional, online, and hybrid formats. She actively attends & presents at educational events and is the proud parent of four adult children, including three educators.

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August 12, 2025
The Collaborative AI Classroom: Teaching Students to Work With, Not Against, AI Tools – Faculty Focus

In today’s rapidly evolving educational landscape, artificial intelligence (AI) has transformed from a distant technological concept to an everyday tool accessible to our students. In recent years, AI has proven to be a disruptor in the delivery of education, raising concerns among educators. Rather than viewing AI as a threat to academic integrity or a shortcut that undermines learning, educators have an opportunity to reframe AI as a collaborative partner in the educational process (Bowen and Watson, 2024). This article examines practical approaches for teaching students to collaborate with AI tools in a manner that enhances, rather than replaces, critical thinking and deep learning.

The Collaborative Mindset Shift

According to the Global AI Student Survey by the Digital Education Council, 86% of students already use AI in their studies (“Digital Education Council Global AI Student Survey 2024,” n.d.). However, with widespread apprehension about the effects of AI in education, a mindset shift may be beneficial in alleviating fears of the unknown. The first step in creating a collaborative AI classroom is to shift both the instructor’s and student’s mindsets. As Bowen and Watson (2024) note in their book, Teaching with AI, ‘AI is going to change our relationship with thinking. It is already challenging ideas about creativity and originality, and it will forever alter education, work, and even how we think about thinking.’ They further posit that students and educators can work with generative AI systems to improve learning processes, making them faster and more efficient. They propose that assessments should shift to focus on the process of creation rather than just the final product. For instance, students might be required to submit transcripts of their AI interactions as part of their assignments, making the learning journey more transparent and accessible.

This approach aligns with Atchley et al. (2024), who conclude that while the new AI technologies in education will initially make educators’ jobs challenging, AI should be viewed as a collaborative tool for teachers and students alike. Their review examines the adoption of AI in education from the perspective that the primary outcome of higher education is employment, that education is centered around the cognitive domains associated with learning, and that there is a risk associated with allowing technology to perform cognitive tasks. Keeping those factors in mind, they posit that ‘to maximize the benefits of collaborative learning (between fully human teams and between teams that include AI), pedagogical strategies must intentionally incorporate primary and secondary factors that encourage shared responsibility, interaction, and metacognitive skill development, enhancing student engagement and learning outcomes.’ Further, they argue that ‘as students enter a workplace, their ability to work on human/computer “teams” is a critical cognitive skillset.’

Practical Classroom Strategies

1. AI-Assisted Drafting and Revision

Instead of banning AI writing tools, consider assignments where students deliberately use AI to generate initial drafts, then critically analyze and substantially revise those drafts. This process teaches students to:

Craft effective prompts (a transferable skill)

Identify strengths and weaknesses in AI-generated content

Apply their own expertise and voice to improve upon machine outputs

Students can submit both the original AI output and their revised version, along with a reflection on their editing process. In addition, asking students to support their edits with evidence-based references teaches them to critically analyze AI outputs. This transparency transforms potential academic dishonesty into a learning opportunity about the value of human expertise and judgment.

2. Comparative Analysis Activities

Design activities where students compare multiple AI-generated responses to the same prompt and then evaluate which is most effective and why. This approach:

Develops critical evaluation skills

Demonstrates AI limitations and inconsistencies

Reinforces that AI requires human oversight

For example, students might prompt three different AI tools with the same question and then analyze differences in factual accuracy, reasoning approaches, and potential biases.

3. AI as Research Assistant

Teach students to use AI as a research brainstorming tool while maintaining scholarly rigor. Students can:

Use AI to generate potential research questions

Identify gaps in AI knowledge that require traditional research

Cross-check AI-suggested sources with academic databases

This approach, similar to what Cianciolo and Regehr (2019) describe as “layered analysis,” helps students understand the complementary relationship between AI-assisted and traditional research methods.

Evidence of Impact

Early evidence suggests that collaborative AI approaches improve learning outcomes. In a study of healthcare education environments, students who learned to critically engage with AI tools, such as simulated gaming (SG), expressed more satisfaction with the training session than the traditional teaching group (TT). Additionally, students in the SG group found the activity more engaging and reported increased motivation (Blanie et al., 2020).

Similarly, in a recent meta-analysis examining the relationship between AI and students’ academic achievement, Dong et al. (2025) concluded that students who utilized AI-enhanced learning approaches demonstrated significantly higher academic performance, outpacing their counterparts in conventional educational settings. Moreover, the authors concluded that AI technologies may be particularly valuable for secondary and higher education students, who typically possess more developed critical thinking skills and can engage meaningfully with sophisticated AI systems that may apply across disciplines.

Implementation Considerations

When implementing collaborative AI approaches, consider these guidelines:

Be explicit about when and how AI use is appropriate for specific assignments

Create rubrics that evaluate students’ critical engagement with AI, not just the final products

Model appropriate AI collaboration in your teaching practices

Provide scaffolded opportunities for students to practice AI collaboration with feedback

Conclusion

By teaching students to work with, rather than against, AI tools, we prepare them for a future where human-AI collaboration will be commonplace across various professions. The collaborative AI classroom doesn’t diminish the value of human thinking, instead, it elevates it by helping students understand what uniquely human perspectives they bring to problems that AI alone cannot solve.

Rather than focusing solely on outcomes (what students produce), this approach emphasizes the learning process itself, aligning with calls from Allen et al. (2021) for more comprehensive evaluation approaches in education that go beyond simple outcome measures. By teaching students to be thoughtful collaborators with AI, we help them develop the critical thinking skills and technological literacy they’ll need throughout their careers.

Ewa Posorski is an educational leader and advocate for integrating artificial intelligence into modern pedagogy. With a focus on fostering collaboration between students and AI tools, Ewa champions a shift in mindset that embraces AI as a partner in learning rather than a threat to academic integrity. Her work emphasizes practical strategies for AI-assisted drafting, comparative analysis, and research support, all aimed at enhancing critical thinking and metacognitive skills. Drawing on current research and classroom evidence, she promotes transparent, process-oriented learning that prepares students for a future of human-AI collaboration across disciplines.

References

Allen, L. M., Hay, M., & Palermo, C. (2021). Evaluation in health professions education—Is measuring outcomes enough? Medical Education, 56(1), 127-136. https://doi.org/10.1111/medu.14654

Atchley, P., Pannell, H., Wofford, K., Hacker, D. J., & Risko, E. F. (2024). Human and AI collaboration in the higher education environment: opportunities and concerns. Cognitive Research: Principles and Implications, 9, 20. https://doi.org/10.1186/s41235-024-00547-9

Blanié, A., Amorim, M. A., & Benhamou, D. (2020). Comparative value of simulation by gaming and a traditional teaching method to improve clinical reasoning skills necessary to detect patient deterioration: A randomized study in nursing students. BMC Medical Education, 20, 53. https://doi.org/10.1186/s12909-020-1939-6

Bowen, J.A. & Watson, C.E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.

Cianciolo, A. T., & Regehr, G. (2019). Learning Theory and Educational Intervention: Producing Meaningful Evidence of Impact Through Layered Analysis. Academic Medicine: Journal of the Association of American Medical Colleges, 94(6), 789–794. https://doi.org/10.1097/ACM.0000000000002591

Bowen, José Antonio, and C. Edward Watson. 2024. Teaching with AI: A Practical Guide to a New Era of Human Learning. Baltimore, Maryland: Johns Hopkins University Press.

Digital Education Council. “Digital Education Council Global AI Student Survey 2024.” n.d. Accessed March 24, 2025. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024.

Liu, D., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers and Education: Artificial Intelligence. https://www.sciencedirect.com/science/article/pii/S2666920X25000402

Source link

August 1, 2025
The Collaborative AI Classroom: Teaching Students to Work With, Not Against, AI Tools – Faculty Focus

In today’s rapidly evolving educational landscape, artificial intelligence (AI) has transformed from a distant technological concept to an everyday tool accessible to our students. In recent years, AI has proven to be a disruptor in the delivery of education, raising concerns among educators. Rather than viewing AI as a threat to academic integrity or a shortcut that undermines learning, educators have an opportunity to reframe AI as a collaborative partner in the educational process (Bowen and Watson, 2024). This article examines practical approaches for teaching students to collaborate with AI tools in a manner that enhances, rather than replaces, critical thinking and deep learning.

The Collaborative Mindset Shift

According to the Global AI Student Survey by the Digital Education Council, 86% of students already use AI in their studies (“Digital Education Council Global AI Student Survey 2024,” n.d.). However, with widespread apprehension about the effects of AI in education, a mindset shift may be beneficial in alleviating fears of the unknown. The first step in creating a collaborative AI classroom is to shift both the instructor’s and student’s mindsets. As Bowen and Watson (2024) note in their book, Teaching with AI, ‘AI is going to change our relationship with thinking. It is already challenging ideas about creativity and originality, and it will forever alter education, work, and even how we think about thinking.’ They further posit that students and educators can work with generative AI systems to improve learning processes, making them faster and more efficient. They propose that assessments should shift to focus on the process of creation rather than just the final product. For instance, students might be required to submit transcripts of their AI interactions as part of their assignments, making the learning journey more transparent and accessible.

This approach aligns with Atchley et al. (2024), who conclude that while the new AI technologies in education will initially make educators’ jobs challenging, AI should be viewed as a collaborative tool for teachers and students alike. Their review examines the adoption of AI in education from the perspective that the primary outcome of higher education is employment, that education is centered around the cognitive domains associated with learning, and that there is a risk associated with allowing technology to perform cognitive tasks. Keeping those factors in mind, they posit that ‘to maximize the benefits of collaborative learning (between fully human teams and between teams that include AI), pedagogical strategies must intentionally incorporate primary and secondary factors that encourage shared responsibility, interaction, and metacognitive skill development, enhancing student engagement and learning outcomes.’ Further, they argue that ‘as students enter a workplace, their ability to work on human/computer “teams” is a critical cognitive skillset.’

Practical Classroom Strategies

1. AI-Assisted Drafting and Revision

Instead of banning AI writing tools, consider assignments where students deliberately use AI to generate initial drafts, then critically analyze and substantially revise those drafts. This process teaches students to:

Craft effective prompts (a transferable skill)

Identify strengths and weaknesses in AI-generated content

Apply their own expertise and voice to improve upon machine outputs

Students can submit both the original AI output and their revised version, along with a reflection on their editing process. In addition, asking students to support their edits with evidence-based references teaches them to critically analyze AI outputs. This transparency transforms potential academic dishonesty into a learning opportunity about the value of human expertise and judgment.

2. Comparative Analysis Activities

Design activities where students compare multiple AI-generated responses to the same prompt and then evaluate which is most effective and why. This approach:

Develops critical evaluation skills

Demonstrates AI limitations and inconsistencies

Reinforces that AI requires human oversight

For example, students might prompt three different AI tools with the same question and then analyze differences in factual accuracy, reasoning approaches, and potential biases.

3. AI as Research Assistant

Teach students to use AI as a research brainstorming tool while maintaining scholarly rigor. Students can:

Use AI to generate potential research questions

Identify gaps in AI knowledge that require traditional research

Cross-check AI-suggested sources with academic databases

This approach, similar to what Cianciolo and Regehr (2019) describe as “layered analysis,” helps students understand the complementary relationship between AI-assisted and traditional research methods.

Evidence of Impact

Early evidence suggests that collaborative AI approaches improve learning outcomes. In a study of healthcare education environments, students who learned to critically engage with AI tools, such as simulated gaming (SG), expressed more satisfaction with the training session than the traditional teaching group (TT). Additionally, students in the SG group found the activity more engaging and reported increased motivation (Blanie et al., 2020).

Similarly, in a recent meta-analysis examining the relationship between AI and students’ academic achievement, Dong et al. (2025) concluded that students who utilized AI-enhanced learning approaches demonstrated significantly higher academic performance, outpacing their counterparts in conventional educational settings. Moreover, the authors concluded that AI technologies may be particularly valuable for secondary and higher education students, who typically possess more developed critical thinking skills and can engage meaningfully with sophisticated AI systems that may apply across disciplines.

Implementation Considerations

When implementing collaborative AI approaches, consider these guidelines:

Be explicit about when and how AI use is appropriate for specific assignments

Create rubrics that evaluate students’ critical engagement with AI, not just the final products

Model appropriate AI collaboration in your teaching practices

Provide scaffolded opportunities for students to practice AI collaboration with feedback

Conclusion

By teaching students to work with, rather than against, AI tools, we prepare them for a future where human-AI collaboration will be commonplace across various professions. The collaborative AI classroom doesn’t diminish the value of human thinking, instead, it elevates it by helping students understand what uniquely human perspectives they bring to problems that AI alone cannot solve.

Rather than focusing solely on outcomes (what students produce), this approach emphasizes the learning process itself, aligning with calls from Allen et al. (2021) for more comprehensive evaluation approaches in education that go beyond simple outcome measures. By teaching students to be thoughtful collaborators with AI, we help them develop the critical thinking skills and technological literacy they’ll need throughout their careers.

Ewa Posorski is an educational leader and advocate for integrating artificial intelligence into modern pedagogy. With a focus on fostering collaboration between students and AI tools, Ewa champions a shift in mindset that embraces AI as a partner in learning rather than a threat to academic integrity. Her work emphasizes practical strategies for AI-assisted drafting, comparative analysis, and research support, all aimed at enhancing critical thinking and metacognitive skills. Drawing on current research and classroom evidence, she promotes transparent, process-oriented learning that prepares students for a future of human-AI collaboration across disciplines.

References

Allen, L. M., Hay, M., & Palermo, C. (2021). Evaluation in health professions education—Is measuring outcomes enough? Medical Education, 56(1), 127-136. https://doi.org/10.1111/medu.14654

Atchley, P., Pannell, H., Wofford, K., Hacker, D. J., & Risko, E. F. (2024). Human and AI collaboration in the higher education environment: opportunities and concerns. Cognitive Research: Principles and Implications, 9, 20. https://doi.org/10.1186/s41235-024-00547-9

Blanié, A., Amorim, M. A., & Benhamou, D. (2020). Comparative value of simulation by gaming and a traditional teaching method to improve clinical reasoning skills necessary to detect patient deterioration: A randomized study in nursing students. BMC Medical Education, 20, 53. https://doi.org/10.1186/s12909-020-1939-6

Bowen, J.A. & Watson, C.E. (2024). Teaching with AI: A practical guide to a new era of human learning. Johns Hopkins University Press.

Cianciolo, A. T., & Regehr, G. (2019). Learning Theory and Educational Intervention: Producing Meaningful Evidence of Impact Through Layered Analysis. Academic Medicine: Journal of the Association of American Medical Colleges, 94(6), 789–794. https://doi.org/10.1097/ACM.0000000000002591

Bowen, José Antonio, and C. Edward Watson. 2024. Teaching with AI: A Practical Guide to a New Era of Human Learning. Baltimore, Maryland: Johns Hopkins University Press.

Digital Education Council. “Digital Education Council Global AI Student Survey 2024.” n.d. Accessed March 24, 2025. https://www.digitaleducationcouncil.com/post/digital-education-council-global-ai-student-survey-2024.

Liu, D., Tang, X., & Wang, X. (2025). Examining the effect of artificial intelligence in relation to students’ academic achievement: A meta-analysis. Computers and Education: Artificial Intelligence. https://www.sciencedirect.com/science/article/pii/S2666920X25000402

Source link

August 1, 2025
Artificial Intelligence and Critical Thinking in Higher Education: Fostering a Transformative Learning Experience for Students – Faculty Focus

In a digitally-driven world, artificial intelligence (AI) has become the latest technology that either will save or doom the planet depending on who you speak with. Remember when telephones (the ones that hung on the wall) were dubbed as privacy invaders? Even the radio, television, and VHS tapes were feared at the beginning of their existence. Artificial intelligence is no different, but how can we ease the minds of those educators who have trouble embracing the newest innovation in emerging technologies? A shift in the fundamental mindset of educators and learners will be vitally important as AI becomes more and more commonplace. To guide this transformative learning process, critical thinking will become an invaluable commodity.

The critical thinking model developed by Dr. Richard Paul and Dr. Linda Elder is pragmatic and fosters the critical thinking skills needed to navigate AI. Critical thinking is defined by Paul and Elder as “the art of analyzing and evaluating thought processes with a view to improving them” (Paul and Elder 2020, 9). The key is to teach your students ways to improve their thinking and using the Paul and Elder model can be an effective tool.

Navigating the Disorienting Dilemma

As we reason through this innovative technology, we will question truth and reality. Teaching students to analyze critically the information generated from AI chatbots will become necessary for a progressing society. Determining fact from fiction will be a skill that dedicated educators will train their students to harness in the work they complete.

Mezirow (1994, 224) contended that a transformative learning experience starts with a disorienting dilemma that causes an individual to question their understanding of previous assumptions by critically reflecting, validating the critical reflection with insight, and acting upon the new information. I, like many I assume, believed that artificial intelligence was a far-fetched concept that would only be real in the movies; however, AI is here and large-language models, such as Chat-GPT and Gemini, are only going to get more sophisticated with time. I also realized that once I was exposed to the Paul and Elder model for critical thinking in grad school, I was ignorant. I had my transformative moment when I realized that critical thinking is more complex than I thought and that I would need to step up my thinking game if I wanted to become an advanced thinker. Artificial intelligence will challenge even the most confident thinkers. Determining fact from fiction will be the disorienting dilemma that will lead us on this transformative journey. As educators, three strategies that we can use to support this transformation with students are to step up our thinking game, model critical thinking, and use AI for our benefit.

Step Up Your Thinking Game

An advanced thinker not only poses questions to others but focuses within. Understanding the why behind reasoning, acknowledging personal biases and assumptions, and valuing other’s perspectives are key to developing critical thinking skills. The reason that you and your students choose to use AI should be clear and intentional. AI is a tool that produces instantaneous solutions. The resulting details from AI should be analyzed for accuracy, logic, and bias. Results should be compared with multiple sources to ensure that the information, the conclusions, and the implications are precise and complete. Practicing these strategies fosters the development of intellectual virtues, such as intellectual humility, intellectual autonomy, and intellectual integrity.

Model Critical Thinking

As educators, we serve as leaders. Ultimately, our students look up to us and use our guidance in their learning. By modeling critical thinking with students, you are leading the way to fostering intentional questioning, ethical principles, and reflective practices. A start is to change the focus of your teaching from the expectation that students regurgitate information to focusing on more challenging, thought-provoking content that fosters thinking. AI can be a helpful tool for coming up with ideas, helping to shape lesson plans, and designing activities, but the real work will come from designing authentic questions that students can be trained to ask regardless of what the AI generates, such as:

How can I verify the validity and accuracy of this information?

Does the response represent logical and in-depth details?

Is the information precise, significant, and relevant to the knowledge that I am seeking?

Are perspectives that differ from mine represented or can I recognize bias in the information?

What other questions could be asked to dive deeper and more concisely into the information?

Use AI for Your Benefit

Generating activity ideas or lesson plans, creating rubrics, and assisting with basic writing tasks are three ways to easily get started with an AI chatbot. If the output is not what you expected or is incomplete, continue to give the chatbot more information to drive the chatbot to produce the desired outcome. Once you begin practicing with an AI chatbot, achieving your desired outcomes will become second nature.

Using learning outcomes as the basis for an inquiry provides AI with the information needed to generate an activity or lesson plan in seconds with objectives, timed components, suggestions for implementation, a materials list, closing, and follow-up ideas for the activity. Try typing a statement into an AI chatbot, such as ChatGPT or Gemini, and be amazed at the magic. When formulating your inquiry, remember to start with the end goal in mind and describe to AI the output you want. For example, type “Use this learning outcome to create an activity: (add learning outcome)”. AI will generate a comprehensive activity with all the bells and whistles.

Rubric development can be a cumbersome process; therefore, using AI to generate a rubric for a project that you have poured sweat and tears into creating is a very simple and time-saving process. Ask AI to generate a rubric based on the information and directions that you give your students. If the generated rubric is not the right style or in the right format, simply refocus the AI chatbot by being more explicit in your instructions. For example, you may need to be as specific as “Create an analytic rubric with 100, 90, 80, 70, and 0 as the levels of performance using the following expectations for the assignment (paste directions and outcomes). As always, use your critical thinking skills to evaluate the rubric and edit it to best meet your needs before sharing it with students.

Use AI to assist you with generating clearer and more concise messages. When creating an email, giving student feedback, or writing in general, a quick and easy way to use AI to assist you is to give the command “make this sound better” and plop in your message. When teaching your students to use AI, have them question the output that was generated. For example, “Why is this statement more clear and concise than my original thought” or “What can I learn from how AI changed my verbiage?” Focusing on the “why” of the produced information will be the key to fostering critical thinking with your students.

AI is a resourceful and impactful, yet imperfect tool. Fostering critical thinking with your students will help them develop the skills needed to recognize bias, inaccuracies, and AI hallucinations. With the practice of creating specific instructions and questioning the outcome, students will learn to trust themselves to defend AI-generated information.

Dr. Tina Evans earned her Ed.D. in Adult Education from Capella University in 2024. With over 25 years of experience in the education field, she brings deep expertise in higher education curriculum design, technology integration, and evidence-based practices for adult learners. Driven by a passion for critical thinking and a genuine commitment to supporting others, Dr. Evans continues to make a meaningful impact in both her professional and personal spheres.

References

Mezirow, J. 1994. “Understanding Transformation Theory.” Adult Education Quarterly, 44 (4): 222-232. https://journals.sagepub.com/doi/10.1177/074171369404400403

Paul, Richard, and Elder, Linda. 2020. The Miniature Guide to Critical Thinking: Concepts and Tools, 8th ed. Rowman & Littlefield.

Source link

July 14, 2025
Artificial Intelligence and Critical Thinking in Higher Education: Fostering a Transformative Learning Experience for Students – Faculty Focus

In a digitally-driven world, artificial intelligence (AI) has become the latest technology that either will save or doom the planet depending on who you speak with. Remember when telephones (the ones that hung on the wall) were dubbed as privacy invaders? Even the radio, television, and VHS tapes were feared at the beginning of their existence. Artificial intelligence is no different, but how can we ease the minds of those educators who have trouble embracing the newest innovation in emerging technologies? A shift in the fundamental mindset of educators and learners will be vitally important as AI becomes more and more commonplace. To guide this transformative learning process, critical thinking will become an invaluable commodity.

The critical thinking model developed by Dr. Richard Paul and Dr. Linda Elder is pragmatic and fosters the critical thinking skills needed to navigate AI. Critical thinking is defined by Paul and Elder as “the art of analyzing and evaluating thought processes with a view to improving them” (Paul and Elder 2020, 9). The key is to teach your students ways to improve their thinking and using the Paul and Elder model can be an effective tool.

Navigating the Disorienting Dilemma

As we reason through this innovative technology, we will question truth and reality. Teaching students to analyze critically the information generated from AI chatbots will become necessary for a progressing society. Determining fact from fiction will be a skill that dedicated educators will train their students to harness in the work they complete.

Mezirow (1994, 224) contended that a transformative learning experience starts with a disorienting dilemma that causes an individual to question their understanding of previous assumptions by critically reflecting, validating the critical reflection with insight, and acting upon the new information. I, like many I assume, believed that artificial intelligence was a far-fetched concept that would only be real in the movies; however, AI is here and large-language models, such as Chat-GPT and Gemini, are only going to get more sophisticated with time. I also realized that once I was exposed to the Paul and Elder model for critical thinking in grad school, I was ignorant. I had my transformative moment when I realized that critical thinking is more complex than I thought and that I would need to step up my thinking game if I wanted to become an advanced thinker. Artificial intelligence will challenge even the most confident thinkers. Determining fact from fiction will be the disorienting dilemma that will lead us on this transformative journey. As educators, three strategies that we can use to support this transformation with students are to step up our thinking game, model critical thinking, and use AI for our benefit.

Step Up Your Thinking Game

An advanced thinker not only poses questions to others but focuses within. Understanding the why behind reasoning, acknowledging personal biases and assumptions, and valuing other’s perspectives are key to developing critical thinking skills. The reason that you and your students choose to use AI should be clear and intentional. AI is a tool that produces instantaneous solutions. The resulting details from AI should be analyzed for accuracy, logic, and bias. Results should be compared with multiple sources to ensure that the information, the conclusions, and the implications are precise and complete. Practicing these strategies fosters the development of intellectual virtues, such as intellectual humility, intellectual autonomy, and intellectual integrity.

Model Critical Thinking

As educators, we serve as leaders. Ultimately, our students look up to us and use our guidance in their learning. By modeling critical thinking with students, you are leading the way to fostering intentional questioning, ethical principles, and reflective practices. A start is to change the focus of your teaching from the expectation that students regurgitate information to focusing on more challenging, thought-provoking content that fosters thinking. AI can be a helpful tool for coming up with ideas, helping to shape lesson plans, and designing activities, but the real work will come from designing authentic questions that students can be trained to ask regardless of what the AI generates, such as:

How can I verify the validity and accuracy of this information?

Does the response represent logical and in-depth details?

Is the information precise, significant, and relevant to the knowledge that I am seeking?

Are perspectives that differ from mine represented or can I recognize bias in the information?

What other questions could be asked to dive deeper and more concisely into the information?

Use AI for Your Benefit

Generating activity ideas or lesson plans, creating rubrics, and assisting with basic writing tasks are three ways to easily get started with an AI chatbot. If the output is not what you expected or is incomplete, continue to give the chatbot more information to drive the chatbot to produce the desired outcome. Once you begin practicing with an AI chatbot, achieving your desired outcomes will become second nature.

Using learning outcomes as the basis for an inquiry provides AI with the information needed to generate an activity or lesson plan in seconds with objectives, timed components, suggestions for implementation, a materials list, closing, and follow-up ideas for the activity. Try typing a statement into an AI chatbot, such as ChatGPT or Gemini, and be amazed at the magic. When formulating your inquiry, remember to start with the end goal in mind and describe to AI the output you want. For example, type “Use this learning outcome to create an activity: (add learning outcome)”. AI will generate a comprehensive activity with all the bells and whistles.

Rubric development can be a cumbersome process; therefore, using AI to generate a rubric for a project that you have poured sweat and tears into creating is a very simple and time-saving process. Ask AI to generate a rubric based on the information and directions that you give your students. If the generated rubric is not the right style or in the right format, simply refocus the AI chatbot by being more explicit in your instructions. For example, you may need to be as specific as “Create an analytic rubric with 100, 90, 80, 70, and 0 as the levels of performance using the following expectations for the assignment (paste directions and outcomes). As always, use your critical thinking skills to evaluate the rubric and edit it to best meet your needs before sharing it with students.

Use AI to assist you with generating clearer and more concise messages. When creating an email, giving student feedback, or writing in general, a quick and easy way to use AI to assist you is to give the command “make this sound better” and plop in your message. When teaching your students to use AI, have them question the output that was generated. For example, “Why is this statement more clear and concise than my original thought” or “What can I learn from how AI changed my verbiage?” Focusing on the “why” of the produced information will be the key to fostering critical thinking with your students.

AI is a resourceful and impactful, yet imperfect tool. Fostering critical thinking with your students will help them develop the skills needed to recognize bias, inaccuracies, and AI hallucinations. With the practice of creating specific instructions and questioning the outcome, students will learn to trust themselves to defend AI-generated information.

Dr. Tina Evans earned her Ed.D. in Adult Education from Capella University in 2024. With over 25 years of experience in the education field, she brings deep expertise in higher education curriculum design, technology integration, and evidence-based practices for adult learners. Driven by a passion for critical thinking and a genuine commitment to supporting others, Dr. Evans continues to make a meaningful impact in both her professional and personal spheres.

References

Mezirow, J. 1994. “Understanding Transformation Theory.” Adult Education Quarterly, 44 (4): 222-232. https://journals.sagepub.com/doi/10.1177/074171369404400403

Paul, Richard, and Elder, Linda. 2020. The Miniature Guide to Critical Thinking: Concepts and Tools, 8th ed. Rowman & Littlefield.

Source link

July 14, 2025
5 Steps to Update Assignments to Foster Critical Thinking and Authentic Learning in an AI Age – Faculty Focus

I recently attended a student panel on use of AI in college classes. The three panelists shared their perspectives, borne out in an April 2025 Gallup survey: they want to learn to use AI tools in appropriate ways, they’re anxious about the potential negative impact on their critical thinking skills, and they want explicit guidance from their faculty members—which they’re not consistently getting.

Even, or especially, in an AI age, we know our students need to think critically and learn authentically to be productive and engaged citizens and future employees. We recognize we need to update assignments to promote academic integrity in an AI age, yet many of us feel unprepared to do so. The crux of the problem is this: how do we foster critical thinking and authentic learning when it’s so easy for students to outsource their cognitive work?

One promising solution to the triple challenge of fostering critical thinking, meaningful learning, and academic integrity is to double down on transparency. We can provide the guidance students want, embed analysis and evaluation into our assignments to get at that all-important critical thinking, and nudge students toward integrity. How? By embracing transparency.

Let’s detail our expectations for responsible AI use. Let’s emphasize the why: why we want students to use their own cognitive abilities for some tasks, why using AI could be helpful at times, and why we’ve crafted our AI-integrated assignments in the ways we have. Finally, let’s ask students to disclose their use in full transparency (and reflect on their learning, as Marc Watkins argues here).

In short, we can promote integrity when assessments are meaningful, purposeful, and include transparent instruction on appropriate AI use. We can teach students to critically analyze AI-assisted work and promote authentic learning as we do. To help us achieve these aims, here are five steps to update assignments in ways that help students learn the skills they need while fostering a culture of academic honesty.

Step 1: Take a critical look at your current syllabus.

If you’re like me, you have tried-and-true activities and assignments that you’ve relied on for many years. Some of these may be updateable, but others may just need to go. If AI can easily complete a task (try running your instructions through a tool like ChatGPT to find out), maybe it’s no longer a relevant measure of authentic learning. Also, following the remaining steps below will likely mean that you add new instructional practices (like modelling AI use) and new components of the assignments you update (like a reflection on how AI helped or hindered learning). Make time for these important new course elements by getting rid of outdated tasks.

Step 2: Consider whether and how students should use AI on the assignments you keep.

Remember, students want to know exactly what is appropriate for AI use in your class. A helpful tool for this process is the 5-level AI Assessment Scale (AIAS). The levels range from No AI, AI Planning, AI Collaboration, Full AI, and AI Exploration. Each one identifies and sanctions different ways students can use AI in appropriate and meaningful ways to support their learning.

Leon Furze, one of the creators of the AIAS, presents it a tool that helps update our assignments and start conversations with our students. He recommends using this framework to guide your thinking: break down each assignment, project, or discussion board activity into disparate steps, then decide for which parts of the process AI use is appropriate. Providing transparent guidance, guidance that may vary for different parts of the project, is what students need right now.

Step 3: Carve out time to discuss and model your expectations.

Many students are not sure what is acceptable in this current moment. What better way to help them feel confident while developing the AI skills they need than modelling what you’re looking for? Take class time or record a video for your online class to teach your students what you expect them to do with AI for each assignment, what not to do, and what you’ll be looking for in their finished product.

As you approach this aspect of instruction, take heart. I don’t think every instructor needs to be an AI expert, but I do think we’re doing our students a disservice if we’re not teaching them how to be AI Whisperers, a concept I gleaned from Ethan Mollick’s 2024 book, Co-Intelligence. A new kind of expertise is emerging, he argues: the skilled and effective use of AI in every field. As disciplinary experts, you know what kind of AI use will help students learn your material and what will disempower them as learners. Let’s allocate time to help students develop AI literacy while maintaining their agency.

Step 4: Ask students to disclose their AI use, as noted above.

The student panelists showed palpable relief when they described instructors who simply had them cite their use of AI. “Encourage your students to be transparent about their AI use,” one student, a finance major, said. As she talked about how simple it was to add a line at the end of the assignment such as, “ChatGPT generated the outline for this essay,” the anxiety that had previously clouded her expression disappeared.

One approach is to use the AI Disclosure (AID) framework to document how students used AI, or add an appendix to each assignment, or add comments or footnotes to make transparent what they wrote and what AI wrote. Get students to declare their AI use and even better, ask them to reflect on whether their use helped or hindered their learning.

Step 5: If you suspect inappropriate use of AI, don’t accuse students of cheating.

Instead, have a conversation with them. Recall that a primary goal of the AIAS is to facilitate discussions about AI use. Imagine that as a class, you agreed that Level 2 (AI Planning) was appropriate for a particular project. If a student submits work that seems to have been completed at Level 4 (Full AI), refer back to the framework to facilitate a conversation about your perception of their work and their process. As AIAS co-creator Leon Furze points out, now you can get away from wondering whether a student cheated with AI and instead ask, “can I make a valid judgement of this student’s capabilities [whether they have used AI or not]?”

And as argued by the authors of a new book titled The Opposite of Cheating: Teaching for Integrity in an Age of AI, this takes moral accusation out of the conversation, reduces the adversarial tone, and enables students to reflect on and hopefully learn from the experience. You can also decide together what actions should take place next, whether the student resubmits the assignment in accordance with your AI-usage guidance, whether they lose the opportunity to earn a grade for that assignment, or whatever else you and your student decide is appropriate in that situation.

Students want to know how to use AI well. “If we don’t teach students how to use AI responsibly, they’re going to be poorly equipped for the workforce,” said the third student panelist, a computer science major. Let’s update our assignments to facilitate meaningful learning of our content, the skills students need in an AI age, and ethical, integrous use of AI to equip them for the workplace they’ll enter. In doing so, we’re helping to develop engaged citizens who think critically about information they encounter. I can’t think of more important work to be engaged in at this moment in time.

Flower Darby is an associate director of the Teaching for Learning Center at the University of Missouri, a co-author of The Norton Guide to Equity-Minded Teaching (2023), and the lead author, with James M. Lang, of Small Teaching Online: Applying Learning Science in Online Classes (2019).

References

Bertram Gallant, Tricia, and David A. Rettinger. 2025. The Opposite of Cheating: Teaching for Integrity in the Age of AI. University of Oklahoma Press.

Furze, Leon. 2024. “Updating the AI Assessment Scale.” August 28. https://leonfurze.com/2024/08/28/updating-the-ai-assessment-scale/

Furze, Leon. 2025. LinkedIn post. https://www.linkedin.com/posts/leonfurze_artificialintelligence-aieducation-assessment-activity-7295247135480889346-UWFC/

Kassorla, M. 2024. “Our New Course: Some Highlights.” January 4. https://michellekassorla.substack.com/p/our-new-course-some-highlights

McMurtrie, Beth. 2025. “AI Unready.” January 30. https://www.chronicle.com/newsletter/teaching/2025-01-30?utm_source=Iterable&utm_medium=email&utm_campaign=campaign_12441556_nl_Teaching_date_20250130&sra=true

Mollick, E. 2024. Co-Intelligence: Living and Working with AI. Portfolio.

Walton Family Foundation and Gallup. 2025. How American Youth View and Use Artificial Intelligence. https://www.gallup.com/analytics/658901/walton-family-foundation-gallup-voices-gen-american-youth.aspx

Watkins, Marc. 2024. “Make AI Part of the Assignment.” October 2. https://www.chronicle.com/article/make-ai-part-of-the-assignment?sra=true

Weaver, Kari D. 2024. “The Artificial Intelligence Disclosure (AID) Framework: An Introduction.” College & Research Libraries News 85 (10). https://crln.acrl.org/index.php/crlnews/article/view/26548/34482

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July 9, 2025
Earning Our AI Literacy License – Faculty Focus

When my son started the fourth grade, his teacher provided a thick bound packet of cursive writing worksheets. She said that completing the packet was optional, but only if completed would her students earn their “pen license” and move from years of pencil use to the holy grail, ink. Using AI should also be earned; earned after becoming AI literate. Banning AI use in college classrooms is a pointless and exhausting endeavor. Instead, we should learn alongside our students to become AI literate. Our society is fully emerged in the AI era with nearly half of US states releasing AI guidance, the state of California signing legislature to include AI literacy in K12 curriculum, and recent guidance from the US Dept of Education on AI implementation in postsecondary education. It is imperative that all of us, students and faculty alike in higher ed institutions, learn AI basics and earn the right to use generative AI responsibly.

So, what does it mean to be AI literate? The answer to this question is changing daily and yet, there are many resources to help our students and us build awareness, knowledge, and skill in AI use. There are online short courses that share many of the same AI literacy basics, including:

Building awareness of how AI is already being used in various fields from healthcare to retail to education and more

Understanding AI vocabulary and acronyms, such as machine learning, deep learning, large language models (LLMs), neural networks, and natural language processing (NLP)

Critique skills to uncover hallucinations, falsehoods, and biases in the output,

Acknowledging privacy, safety, and environmental concerns

Skill in prompt engineering, and 6. knowledge and practice in AI uses in specific contexts

Together, with our students, we can turn to many free online options. Below is a short list I’ve discovered for myself and have provided to my students:

Elements of AI: An online course by MinnaLearn and the University of Helsinki to learn the capabilities of AI through theory and practical exercises.

AI 101 for Teachers: A series of videos, with companion guides, on AI foundations for educators from collaborative partners – Code.org, Educational Testing Service (ETS), International Society for Technology in Education (ISTE), and Khan Academy.

2024 AI Literacy Canvas Module ( Request Form): This AI literacy Canvas module with a Creative Commons CC BY-NC-SA license designed by the Center for Teaching Excellence and Innovation (CTEI at Rush University, covers four areas of AI literacy: recognition, comprehension, critical thinking, and proficiency.

Ultimately, learning AI basics does go beyond short online courses and requires iterative practice using a variety of AI tools. Once we all know the basics, we can use established frameworks to help guide more informed discussions establishing dos and don’ts for responsible AI use in our courses. And yes, some, maybe even many, will abuse the privilege of AI use in your class, and you’ll find a big splatter of blue cross-outs or black ink smudges, but it’s still better than banning pens completely. Let’s provide avenues to improve our awareness, knowledge, and skills in AI use as we all collectively figure out how to manage in a future world where AI will be embedded in our work.

Madeline Craig is an associate professor and technology integration coordinator at Molloy University in Rockville Centre, NY. In improving her own AI knowledge and skill, she relies on her professional learning networks on LinkedIn, Bluesky, and ISTE CONNECT while at the same time playing with a wide variety of AI tools.

References

Klein, A. (2024, September 11). ‘A Solid Start’: States are crafting AI guidance for schools, but have more to do. Education Week. https://www.edweek.org/technology/a-solid-start-states-are-crafting-ai-guidance-for-schools-but-have-more-to-do/2024/09

Lambert, D. (2024, October 4). California students to learn about AI and media literacy soon. EdSource. https://edsource.org/updates/california-students-to-learn-about-ai-and-media-literacy-soon?utm_source=ai-admin.beehiiv.com&utm_medium=newsletter&utm_campaign=the-ace-ai-literacy-framework&_bhlid=ffb2ed35f7f7c84088bcb6b4caa5765a8f6854e1

Miao,F. & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://doi.org/10.54675/ZJTE2084

U.S. Department of Education, Office of Educational Technology. (2024). Navigating Artificial Intelligence in Postsecondary Education: Building Capacity for the Road Ahead, Washington, DC.

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February 12, 2025

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