In science, we build a firm foundation and then we keep renovating the house. We find interesting results, we are fascinated by them, we don’t always trust them, so we go back and replicate them. We also challenge them by asking, where will this not work? When does the effect go away? How can we use better methods to test our theories?
As part of this process, scientists ask each other questions. Recently, other scientists asked us some questions about three of our papers. We took this very seriously, carefully considered each inquiry, delved into the studies again (in some cases reanalyzing the data), and prepared three documents, each detailing our process and our findings (here, here, and here). In each case, we showed that the conclusions reached in the paper were sound. But, as with anything that helps make science better, we were grateful for the questions because they pointed out areas for improvement or clarity, and because we believe in open science.
It is important however to consider these questions in light of a large body of work. The growth mindset story does not rest on a handful of isolated studies. Research in this area has been ongoing for 30 years and the field has amassed a large body of work. A meta-analysis published in 2013 found 113 studies conducted by many authors and concluded that mindsets are a significant factor in people’s self-regulation toward goals.
Meta-analyses are helpful, but not the final word. Government data collected at a country level—all the 10th grade students in the country of Chile (over 160,000)—showed that holding a growth mindset predicted academic achievement at every socioeconomic level. Recently, the state of California, collecting data from over 100,000 middle schoolers, found that students’ mindsets were a good predictor of their test scores. And this doesn’t include many experimental studies that have carefully oriented children (or adults) toward different mindsets and found effects on outcomes. It is highly unlikely that mindset is a phantom phenomenon.
In science, we build a firm foundation and then we keep renovating the house. We find interesting results, we are fascinated by them, we don’t always trust them, so we go back and replicate them. We also challenge them by asking, where will this not work? When does the effect go away? How can we use better methods to test our theories?
As part of this process, scientists ask each other questions. Recently, other scientists asked us some questions about three of our papers. We took this very seriously, carefully considered each inquiry, delved into the studies again (in some cases reanalyzing the data), and prepared three documents, each detailing our process and our findings (here, here, and here). In each case, we showed that the conclusions reached in the paper were sound. But, as with anything that helps make science better, we were grateful for the questions because they pointed out areas for improvement or clarity, and because we believe in open science.
It is important however to consider these questions in light of a large body of work. The growth mindset story does not rest on a handful of isolated studies. Research in this area has been ongoing for 30 years and the field has amassed a large body of work. A meta-analysis published in 2013 found 113 studies conducted by many authors and concluded that mindsets are a significant factor in people’s self-regulation toward goals.
Meta-analyses are helpful, but not the final word. Government data collected at a country level—all the 10th grade students in the country of Chile (over 160,000)—showed that holding a growth mindset predicted academic achievement at every socioeconomic level. Recently, the state of California, collecting data from over 100,000 middle schoolers, found that students’ mindsets were a good predictor of their test scores. And this doesn’t include many experimental studies that have carefully oriented children (or adults) toward different mindsets and found effects on outcomes. It is highly unlikely that mindset is a phantom phenomenon.
Discover the importance of AI literacy and computer science education in preparing students for an AI-driven future. Join the mission to empower creators, not just users, of AI.
With growing pressure to reduce emissions, costs and waste, now is the time for universities to reimagine their role in the resource life cycle, says Darren Wilkinson
A Definition of the ancient Greek term Eudaimonia, sometimes translated as happiness, flourishing, or the good life, as well as views from the Stoics, Epicureans, Cyrenaics and Aristotle on what the good life meant.
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Information for this video gathered from The Stanford Encyclopedia of Philosophy, The Internet Encyclopedia of Philosophy, The Cambridge Dictionary of Philosophy, The Oxford Dictionary of Philosophy and more!
Artificial intelligence has accelerated the Wright’s Law principle. It is rewriting it, which assumes that experience follows production: You make mistakes, learn from them and improve. AI makes it possible for experience to come before production. Simulation can happen millions of times before a single box is shipped. Experience scales almost instantly at no real cost. The learning curve doesn’t only steepen. It collapses.
La Ley de Wright que postula la Curva de Aprendizaje, está colapsando por la proliferación de la IA. Esto significa que el conocimiento que alguna vez tomó décadas de prueba y error humano, ya está surgiendo en semanas, días e incluso horas. El resultado es aún más poderoso cuando la IA se combina con robótica, sensores, inteligencia geoespacial y computación en la nube. Cuando el conocimiento de la IA se infunde con la experiencia de profesionales experimentados, los ciclos de innovación colapsan aún más. El conocimiento se materializará instantáneamente. La innovación y la disrupción llegarán en ondas de choque, no en ciclos. El desafío no será construir las herramientas, sino sobrevivir al ritmo de sus consecuencias.
"For Instructional Design in 2026, data analytics has become an indispensable tool for creating impactful learning experiences, and the field is experiencing explosive growth."
"The most powerful way to use AI is to treat it as a partner that widens the field of ideas while leaving the final call to us. AI can collect data in seconds, sketch multiple paths forward, and expose us to perspectives we might never consider on our own."
"When ChatGPT-3.5 was released in November of 2022, it was immediately clear that education would change forever. It sparked dramatic headlines speculating the effect of the program on higher education, such as “The College Essay Is Dead” from The Atlantic, and opened a world of untapped possibilities for cheating, plagiarism and rampant misinformation that educators were left to restrain. It’s been a few years since the initial launch of ChatGPT, and the advances in subsequent versions show that ChatGPT’s developers have not lost any ambition."
"Given that AI will only become more prevalent in our lives, universities should be taking more formal steps to make sure graduating students are literate in the practical uses of AI and leave college with a well-rounded understanding of the ethical issues surrounding it."
"Cyber attacks are hitting K–12 schools with alarming regularity. From mid-2023 through 2024, more than four out of five reporting districts faced some kind of breach, such as ransomware, stolen data, or network lockouts. And still, one part of the network gets little attention: the printers."
Innovating Your L&D Strategy By Fostering Agility Imagine a company where Learning and Development (L&D) design is swift, training materials are always up to date, and outdated content and formats are addressed before they negatively affect learning outcomes. This is not a science fiction scenario,
"Without a coherent approach that connects neuroscience with EdTech and AI, we risk designing systems that optimize for short-term technological efficiency and long-term human problems."
"For Instructional Design in 2026, data analytics has become an indispensable tool for creating impactful learning experiences, and the field is experiencing explosive growth."
Kiana sat at her desk, multiple browser tabs open — one for scholarships, one for mental health, one forass registration and not a single one that remembered her.
Later, outside the advising office, she scrolled through her phone, hunting for the exact words she'd used in her last intake form because she knew she’d have to say it all again.
She was told AI could help. But when she tried it, she hesitated. Could she upload a transcript? Would her questions be saved? Could someone else see them? She wanted AI to work for her, but she didn’t know if it was safe to trust it.
Trust, it turns out, was earned not just when AI gave a helpful answer, but when the university treated her data as a bridge to her goals. The real measure was whether the system respected her boundaries, protected her privacy and upheld her right to learn without fear.
And in that moment, the future of TechEd came into focus: the platforms we build will only serve students if they are designed to be worthy of their trust.
"The rapid growth of generative artificial intelligence (GenAI) is creating a new digital divide in K–12 education, an AI skills gap that threatens to leave some students behind."
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