Prenostik

Many general education classes help students learn and practice the skills employers want most—critical thinking, creativity, and teamwork. These courses are more than just a graduation requirement; they lay the foundation for lifelong learning and career growth. Colleges must do a better job explaining their value, and professors should bring real-world context into these classes to help students see their relevance beyond just fulfilling a checklist. #HigherEducation #CareerSkills #CriticalThinking #LifelongLearning Highlights from the post: Many students and more than a few professors see general education as unwanted, demanding, dull, and unavoidable. And the fault lies with colleges themselves. Families are asking what they’re getting for a costly, four-year degree. Credentialism has led many students to seek double majors or minors that provide job-ready skills, reducing their patience for poorly thought-out extras. Gen-ed has also been called into question by families and politicians. Less than 30 percent of college graduates work in a career closely related to their major,?and the average worker has 12 jobs in their lifetime. That means, he [David Carballo, Boston University Professor], says undergraduates must learn to be nimble and build transferable skills. Why can’t those skills and ways of thinking be built into general education? For many academics, framing education regarding skills sets off alarm bells. That is the language of ed-tech evangelists, higher-ed skeptics, and alternative-credentials advocates. Recruiters are more likely to list the skills they’re looking for: sometimes instead of a degree, sometimes in addition to a degree. While this might seem a matter of semantics, it points to a fundamental culture shift: A college degree is no longer presumed to confer such abilities. Educators must be more explicit about what they teach students and document it more clearly. For many universities, the new general-education program is more deliberate in design, explicit in its goals, gradually introduced to students’ majors, and subject to regular review. Boston University and Johns Hopkins is replacing the traditional distribution model of general education with six foundational qualities that students must acquire, such as complex creative expression and ethical reasoning. They are replacing the traditional distribution model of general education with six foundational qualities that students must acquire, such as complex creative expression and ethical reasoning. "We need to teach students how to use and critique and synthesize that information.” It’s a massive lift for colleges to improve their general-education programs by offering more-meaningful, relevant, and well-taught courses that provide lifelong skills. But it won’t work unless students see how it all adds up. And that’s a real challenge. If gen ed does its job, the payoff will only arrive years later.

Coaching students to think critically AND creatively helps them become independent problem-solvers, capable of navigating complexity and uncertainty. Instead of simply accepting information given in a classroom (or from anywhere else), students should be encouraged to dissect and challenge them – the hard work needed for authentic learning and true understanding. When students learn to think for themselves, they gain the tools to creatively adapt, innovate, and contribute meaningfully to society. #CriticalThinking #Education #CreativeThinking #GrowthMindset #Innovation #LifelongLearning Selected highlights from the video: The education systems primarily focus on imparting knowledge and memorizing facts ("what to think") rather than explicitly teaching the processes and skills involved in thinking itself. Teaching "what to think" emphasizes acquiring specific information, formulas, and historical facts, often focusing on finding the "right" answer. In contrast, teaching "how to think" involves engaging students in their own learning process by encouraging questioning, fostering understanding (not just knowledge), and cultivating a sense of curiosity. It empowers students to analyze information independently rather than passively receiving it. Learning to think involves two key components: creative and critical thinking. Creative thinking is making new connections and innovating to solve problems – generating new ideas and possibilities. Critical thinking involves rigorously questioning information, forming well-reasoned arguments, identifying flawed logic, and recognizing personal biases. Both are essential because creativity without critical thinking can lead to impractical or misguided ideas, while critical thinking without creativity may lack the ability to generate novel solutions. Creative and critical thinking are vital for navigating the complexities of the modern world and developing solutions to solve problems. By learning to question rigorously, identify logical fallacies, and be aware of their own biases, individuals become more resilient to misleading information. They are empowered to form their own informed opinions to create new ideas.

The AI Paradox: Short-Term Equalizer, Long-Term Divider? In education (and beyond), AI can act as an equalizer in the short term, enabling students (or novice workers) to produce seemingly quality work quickly. However, in the long run, AI can’t replace the challenging mind-bending mental gymnastics required to develop critical thinking and cognitive skills essential for personal growth. Learning is supposed to be difficult. If AI is used as a crutch instead of a tool to enhance human critical thinking, the divide in the workplace will only grow wider. #AI #Criticalthinking #TrueLearning #EdTech #Lifelonglearning Highlights from the article: As large language models first gained popularity in the early 2020s, economists and bosses were hopeful that they, and other?AI?tools,?would level the playing field, with lower-skilled workers benefiting most. More recent findings have cast doubt on this vision, however. They instead suggest a future in which high-flyers fly still higher—and the rest are left behind. In complex tasks such as research and management, new evidence indicates that high performers are best positioned to work with?AI. Evaluating the output of models requires?expertise and good judgment. Rather than narrowing disparities,?AI?is?likely to widen workforce divides, much like past technological revolutions. The case for?AI?as an equaliser was supported by research showing that the tech enhances output the most for less experienced workers. A study in 2023 by Erik Brynjolfsson of Stanford University and Danielle Li and Lindsey Raymond of the Massachusetts Institute of Technology (MIT) found that generative-AI?tools boosted productivity by 34% for novice customer-support workers, helping them resolve queries faster and more effectively. Experienced workers, by contrast, saw little benefit, as the?AI?reinforced approaches they were already using. This suggested the tech could narrow gaps by transferring best practices from talented to less talented employees. Although early studies suggested that lower performers could benefit simply by copying?AI?outputs, newer studies look at more complex tasks, such as scientific research, running a business, and investing money. In these contexts, high performers benefit far more than their lower-performing peers. In some cases, less productive workers see no improvement or even lose ground. Aidan Toner-Rodgers of?MIT, for instance, found that using an?AI?tool to assist with materials discovery nearly doubled the productivity of top researchers while having no measurable impact on the bottom third. The software allowed researchers to specify desired features and generate candidate materials predicted to possess these properties. Elite scientists, armed with plenty of subject expertise, could identify promising suggestions and discard poor ones. Less effective researchers, by contrast, struggled to filter useful outputs from irrelevant ones.

AI tools in education are transforming how students write, but we risk losing a key element: their unique voice, agency, and style. While AI can enhance grammar and structure, it can also strip away the authenticity of a student's work. Authentic learning isn’t just about polished writing—it's about developing a personal point of view by deeply engaging with material through critical thinking. As AI becomes more integrated into the learning process, educators must guide students to see the value of their voices, even if imperfect. Technology should enhance, not replace, their individuality and cognitive growth. PS: Many thanks to UC Irvine professors Daniel Gross and Qian Du for co-authoring the following article with Prenostik CEO Patrick Hong. #AI #CriticalThinking #Voice #AuthenticLearning #EdTech #LifelongLearning Highlights from the post: Engineering students spend most of their time using highly structured mathematics to reach a numbered answer...it’s easy [for them] to think that following rules of spelling, grammar, and paragraph construction will result in good writing. Just as following mathematical formulas will help you reach a technical solution. So, as teachers, we have a new and interesting job of showing students what goes wrong when AI chats back. This lesson is relevant far beyond the classroom. [In writing], voice, agency, and style appear differently in the age of AI. ?“We are used to the idea that people or entities that can express themselves, or manipulate language, are smart – but that’s not true,”?says?Yann LeCun, a New York University professor, senior Meta AI researcher, and a 2019 Turing Award winner. “You can manipulate language and not be smart, and that’s basically what LLMs are demonstrating.” So “voice” isn’t about asserting the grammatical first person, and “style” isn’t about puffing up word strings. (In fact, these linguistic functions can also be prompted easily.) Situation sensitivity, including audience and exigency, is just as important for STEM writing as any other type of writing. We need to teach that imperfect but authentic writing is more valuable than sentences that are polished on the surface. The lingo of writing studies might translate this as "from product to process to person." However, GenAI now appears to do much of the writing process: With minimal human prompting, it can brainstorm, draft, revise, and edit. Thus, new pedagogical insights and practices may emphasize how the writer process is tied to a person's idiosyncrasies.

Relying too much on AI can encourage students to skip the effort required to understand the "why" behind answers. As we make AI smarter, we are susceptible to increasingly trusting AI and becoming reliant on it. As we do so, we can be less inclined to think critically and risk becoming less capable, reversing the roles of "humankind" and "machines." We must encourage students to engage deeply in education and think for themselves, ensuring technology enhances their intellectual growth rather than replaces it. #CriticalThinking #AuthenticLearning #LifelongLearning #EdTech #education Highlights from the post: Large language models are emerging as?transformative educational tools, revolutionizing how students learn, create, and solve problems. Yet, alongside their undeniable benefits, a new challenge has surfaced—"metacognitive laziness." A?recent study (PDF downloadable in the article) in the British Journal of Educational Technology closely examines this phenomenon, exploring how reliance on generative AI impacts self-regulated learning,?intrinsic motivation, and performance. The findings reveal a paradox: while ChatGPT 4.0 (the only model used in this study) enhanced task outcomes, it may have eroded the critical thinking and reflective processes essential for lifelong learning. The Cognitive Benefits of LLMs At their core, LLMs are designed to augment human?intelligence. They provide instant feedback, overcome language barriers, and facilitate?personalized learning experiences. This study found that ChatGPT students significantly improved short-term performance, particularly in essay writing tasks. The AI group outperformed even those guided by human experts, underscoring generative AI's unparalleled efficiency and precision. The Shadow of Cognitive Offloading Despite these advantages, the study highlights a troubling side effect: "metacognitive laziness." This term describes a learner’s tendency to offload cognitive responsibilities onto AI tools, bypassing deeper engagement with tasks. While AI’s ability to handle rote or complex calculations is beneficial, overreliance on it can diminish essential self-regulatory processes such as planning, monitoring, and evaluation. The research observed that students interacting with ChatGPT engaged less in metacognitive activities than those guided by human experts or checklist tools. For instance, learners in the AI group frequently looped back to ChatGPT for feedback rather than reflecting independently. This dependency not only undermines critical thinking but also risks long-term skill stagnation. Moving Beyond the Immediate One of the study's most striking findings was the lack of improvement in knowledge transfer among the AI group. While ChatGPT excelled at boosting task-specific outcomes, it did not enhance learners’ ability to apply knowledge in novel contexts. This underscores the importance of fostering transferable skills—a cornerstone of lifelong learning.

It is more important than ever to coach students to learn by gaining knowledge, unlearn by challenging assumptions, and relearn by embracing new perspectives, all to strengthen critical thinking. This mindset nurtures resilience, turns challenges into growth opportunities, and fosters grit. Most importantly, by continuously learning and adapting, we empower students with the agency to take ownership of their growth, making them more adaptable and prepared for the future. #GrowthMindset #CriticalThinking #Learning #Unlearning #Relearning #Education Highlights from the video: In this TEDx talk, Chris Yeh, a founding partner of Blitzscaling Ventures and an experienced investor, entrepreneur, and writer, emphasizes the importance of being an "infinite learner"—constantly learning, unlearning, and relearning to adapt to a rapidly changing world. To illustrate this, he draws from naval history, using the shift from battleships to aircraft carriers during World War II as a powerful example of unlearning outdated practices and embracing new technology. Battleships were once the backbone of naval power, dominating warfare for nearly 80 years. When the United States was attacked at Pearl Harbor in 1941, the majority of the Pacific Fleet’s battleships were disabled. Admiral William Moffett, who was initially a battleship commander, recognized this shift and led the U.S. Navy’s investment in aircraft carriers, which could launch aircraft to strike enemies from a distance. His willingness to unlearn the dominance of battleships and embrace the future of aviation is a prime example of the cycle of learning, unlearning, and relearning. Chris also contrasts this historical lesson with the rise of Steve Jobs and Apple, demonstrating the same mindset of infinite learning. Jobs didn’t hesitate to discard outdated technologies like the floppy disk and CD-ROM to improve his products, similar to how Moffett abandoned battleships for aircraft carriers. By recognizing when a technology or approach has reached its limits and being willing to move on to something new, both Moffett and Jobs exemplified the importance of unlearning past successes to build a stronger future. This mindset of infinite learning is critical for navigating today’s challenges, such as the rise of artificial intelligence, which is transforming industries like the Industrial Revolution, and technological shifts like the advent of aircraft carriers. Ultimately, Chris advocates for creating a culture of infinite learning within organizations, where individuals support one another in embracing change, failure, and growth through continuous learning, unlearning, and relearning.

Learning is meant to feel challenging. It’s not about memorizing or regurgitating information to pass a test or earn an A. Authentic learning happens when students dig deep to understand the “why” behind every concept–the "rigor." When paired with supportive, thoughtful teaching, this challenge creates a space where students can take intellectual risks, ask meaningful questions, and grow beyond rote knowledge. How do you ensure your learning or teaching practices encourage genuine understanding rather than surface-level mastery? #Education #LifelongLearning #CriticalThinking #AcademicRigor #MeaningfulLearning Highlights from the post: “Dysfunctional Illusions” About Rigor The term “rigor” in academia is often misunderstood, explained Dr. Kevin Gannon [Director of the Center for the Advancement of Faculty Excellence (CAFE) and Professor of History at?Queens University of Charlotte]. Long-held misconceptions about teaching and learning in higher education include: 1) “Hard courses” weed out the weak students”, i.e., when a student fails a rigorous course, it is always due to some deficiency of the student, 2) If too many students are getting A’s, then it must mean that the curriculum has been “dumbed down," 3) Traditional modes of instruction, like teacher-centric lecturing, are effective pedagogy, and active learning techniques “pamper” students (despite evidence to the contrary, particularly in STEM (Freeman et al., 2014b)). 4) Students must always adhere to the pace set by the instructor (Nelson, 2010). Gannon defines "rigor" as how instructors can challenge their students. He contends that a course can embody challenge in two ways:”cognitive” and “logistical.” Gannon argues that faculty often view rigor as consisting of cognitive challenges, the levels at which students engage with the content. If the logistical challenges traditionally associated with rigor prevent the student from completing the cognitive challenges required for success in the course, the instructor, not the student, must adapt. Gannon presented three “things” that advance student learning: Trust.?Learning is inherently social. Consequently, the student’s relationship with their instructor will affect their engagement with the course. When students have trusting relationships with their instructors, they are “.. better able to take advantage of critical feedback and other opportunities” to advance learning (Walton & Cohen, 2007). Transparency.?When instructors are transparent about how a particular course assignment relates to the course’s learning outcomes or concepts, their learning is advanced (Winklemes et al., 2016). Compassionate challenge.?When students are provided with what Sarah Rose Cavanagh (2023) calls “compassionate challenge”– a safe learning environment where they feel a sense of belonging, engage in practices associated with confronting fears, and take intellectual risks in meaningful ways—they advance their learning.

The #education system has primarily been designed as an industrial complex where subjects (components) are taught (manufactured) in isolation for efficiency (less cost and time) with the hope that all can be put together at the end to graduate (churn out) students (products). But in pursuit of efficiency,?the "putting together" or the #criticalthinking connecting everything has been neglected, giving way to students focusing on grades for a specific class subject rather than relating all their learnings to broader life contexts. With?#AI,?#educators?must elevate?#pedagogy and?focus on coaching students to complete the critical thinking training needed to ensure?#lifelongsuccess. Highlights from the post: As educators, our mission is to inspire students to engage deeply with the material we teach, equipping them with the critical-thinking skills they’ll need in a world that changes by the minute. With generative AI in the picture, that mission has become more crucial—and we can even use gen AI as a powerful tool to accomplish it. The three strategies I [article author Michael Roberto] share here...aims to make AI an ally in students’ learning, pushing them to draw insights, evaluate complex ideas, and communicate their conclusions effectively. 1. Design assignments for critical thinking The best assignments are multi-layered, requiring students to draw connections between various concepts and use those connections to form independent conclusions. For example, multi-layered assignments ask students to connect a case study to other readings, ideas that emerged during class discussions, and their own personal experiences. They require students to draw multiple connections and explain their logic, not just their solution or recommendation. These types of exercises promote the best learning, and it’s a bonus that AI can’t do them for our students. 2. Modernize writing assignments for the AI era Writing assignments are perhaps the most common scenarios in which students need guidance in using AI appropriately and effectively. If students use gen AI to do their writing for them, they don’t hone the analytic thinking skills that writing assignments are meant to teach. In the past, I often gave students writing assignments that asked them to summarize basic elements of a case or complete a five forces analysis. However, these are things gen AI can easily do. To guarantee students are working independently, I now ask more involved questions that require students to perform a certain level of independent analysis or to draw connections to class topics. 3. Update case questions for deeper analysis In the age of AI, though, straightforward preparatory questions won’t tell you whether students are adequately prepared to contribute analysis come discussion time. Instead, to get them ready for the deeper thinking required of case discussions, create questions that are broader in scope but that push students to analyze more deeply.

When?#K12?and?#highered?writing assessments overemphasize expression (grammar) rather than the process of developing a personal voice (a point-of-view), #students?will be incentivized to rely on #AI. The flowing AI-written language, mistaken as "the" benchmark for good writing, tempts students to skip the iterative?#criticalthinking?process needed to arrive at their point of view. In doing so, we are signaling that how the words are put together as output is more important than the substance behind them. This is similar to students only caring about getting the correct answer for the grade, not the learning behind it. Highlights from the post: Faculty members who teach writing-intensive classes are frustrated that students are defaulting to AI-written prose. This includes using AI to write discussion posts and essays in which the professor wants to hear the student’s opinions and experiences. The results are often sterile, bland, and airless. But they are spelled and punctuated perfectly, free of grammatical mistakes. Kaye Adkins, a professor of technical communication at Missouri Western State University, wrote about whether some traditional grading methods should be rethought in the age of AI. “Does anyone else find themselves tempted to give a higher grade to a paper than they might have in the past because, although the paper has issues like fragments, run-ons, or poor organization—thus signaling it was not written with AI, the paper shows deep engagement in the topic?” she asked. “In first-year writing courses, I have given such papers Cs or Ds (depending on the point in the semester), but these days, I’m so relieved to see a student actually engage with a topic that it’s tempting to give them a higher grade than I would have pre-AI. “I’ve been part of the content vs. correctness wars of the past,” she continued, “and I’ve always come down on the side of ‘the two can’t really be separated.’ But with AI, I’m wondering if that’s true anymore. Should we be thinking about going back to the bad old days of giving two grades on papers — one for form/ conventions and one for writing?” Of course, context matters, she [Laura Dumin, professor of English and technical writing at the University of Central Oklahoma] notes. A freshman composition course, for example, is one in which the instructor likely wants to encourage critical thinking and original ideas, while a technical writing course emphasizes accuracy and clarity. Developmental writing might be another place, she said, where an emphasis on grammar and sentence structure actually builds confidence in the writer. “If you’ve got students who are coming out of that, who have often been told that their writing wasn’t good enough or wasn’t what the teacher was looking for,” she said, “they may be more tempted to use AI to write things because they feel like that’s what the instructor wants. And I’m very clear with my students that I want to hear their voices, that I privilege their voices.”

In the age of?#AI, it's more important than ever to focus on helping students learn the "why" and the "how" (#criticalthinking) rather than emphasizing the "what"(answers) in?#K12?and?#highered. Care must be exercised when integrating AI into?#education. Otherwise, students will fall into just getting the correct answer from the machine and not adequately critically thinking about the reasons themselves—something we have already been failing at even before AI's omnipresence. Highlights from the post: Large language models are transforming how we work. But as Timandra Harkness [Author of the book?Big Data: Does Size Matter??Moderator of the Data Debate series at the Alan Turing Institute] argues that there are things artificial intelligence cannot do. Economist Education:?In an age of AI, will humans need to think more or less? Timandra Harkness:?We’ll need to think as much as ever, but we’ll need to be more aware of why and how we’re thinking. If you ask AI the wrong question it’s not going to give you the answer you need. Ada Lovelace, the woman who arguably wrote the first computer program...essentially said: “it’s not going to be creative in itself, but it might spur us to be creative, because it will enable us to do things that we hadn’t thought we’d be able to do—to answer scientific questions that we didn’t think we could answer—and so we will have to think more creatively about what we can use it for.” Economist Education:?Can AI think critically? Timandra Harkness:?No, AI can’t think at all. I take a hardline view. For me, thinking is a process that involves some fundamentally human elements, such as purpose. Our thinking is always tied to our goals, desires, and needs. Machines only have the purposes that we give them, and sometimes not even those. Economist Education:?You talk about critical thinking as though it’s a duty. Timandra Harkness: The more people say, “The machines are taking over; the machines are so powerful,” the more you are making that happen by giving up your own agency. I think human adults should always have an eye on the future and what we’d like it to be. And to say, “Oh, I’ll let the technology decide,” would be a real failing. Economist Education:?The world is getting ever more complicated. Do you think human critical thinking can rise to the challenge? Timandra Harkness:?Let’s hope so. It’s vital to think critically about things and try to make sense of them, work out the causes and effects, and have a stab at predicting future possibilities without trying to claim absolute certainty. People and organizations should say: “Well, it is uncertain, but let’s think about why things are happening and, therefore, what we might have good reason to think is going to happen next.” That way, you may be more prepared for it. Critical thinking can equip you to see the possible directions the world may take.