Today's AI agents are a primitive approximation of what agents are meant to be. True agentic AI requires serious advances in reinforcement learning and complex memory.
EDTECH@UTRGV's insight:
"Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be"
The latest news related to the meaningful and effective implementation of educational technology and e-learning in K-12, higher education, corporate and government sectors.
Watch this video to learn more about the fully online, accelerated, project-based Master of Education in Educational Technology at the University of Texas Rio Grande Valley. For more information, visit: https://www.utrgv.edu/edtech/index.htm
EDTECH@UTRGV's insight:
This 30-hour accelerated program designed to prepare persons in K-12, higher education, corporate, and military settings to develop the skills and knowledge necessary for the classrooms and boardrooms of tomorrow. Students in this program have the opportunity to earn one or more graduate certificates in E-Learning, Technology Leadership, and Online Instructional Design.
Desde mi punto de vista, el Master of Education in Education Technology responde de manera acertada a las necesidades actuales del ámbito educativo, donde la integración pedagógica de la tecnología es cada vez más importante. El enfoque basado en proyectos potencia un aprendizaje significativo, ya que permite a los maestros diseñar y aplicar recursos digitales directamente en sus contextos escolares. Además, el formato online y acelerado facilita la actualización profesional continua, lo que considero clave para mejorar la práctica docente y promover una educación más creativa y eficaz.
"Neuroscientist Jared Cooney Horvath's new book and article argue that when we gave students laptops, student performance declined, so the tech broke their brains. That story skips the real culprits:
high-stakes standardized testing that reshaped public schooling, and
inequitable access to effective models of learning, not to devices."
EDTECH@UTRGV's insight:
"Students in well-resourced schools are more likely to experience project-based, passion-driven models where technology is used for real-world work. Students in under-resourced and segregated schools are more likely to sit in “drill and kill” environments, whether the drill is on paper or on a screen."
The digital learning landscape is entering a new phase defined by rapid advances in artificial intelligence, rising expectations for the student experience, and increasing pressure to demonstrate quality and accountability in online education.
EDTECH@UTRGV's insight:
"Artificial intelligence is expected to renew institutional focus on instructional design, as AI-generated content increases the need for structured, pedagogy-informed course design aligned with accessibility, inclusion, and quality standards."
Learn about AI-enhanced learning design and how it balances efficiency with creativity for a better educational experience.
EDTECH@UTRGV's insight:
"AI can support eLearning, but only humans provide the creativity, judgment, and accountability needed for quality learning. Effective Instructional Design depends on humans guiding and refining AI, not the other way around."
A strong and solid AI strategy will enable education leaders to answer what improved, for whom, and under what conditions.
EDTECH@UTRGV's insight:
"A common mistake is shipping capabilities in search of purpose. Chat interfaces, content generation, personalization, and automated feedback can all be useful. Utility is not efficacy."
Teaching via a screen makes it harder to read student understanding and sustain attention. These practical strategies show how educators can keep online students invested
EDTECH@UTRGV's insight:
"[W]ith a multitude of digital distractions vying for students’ attention, online teaching requires intentional pedagogical adjustments."
"Microlearning holds real promise, especially as conversations shift toward using short-form learning more intentionally than doomscrolling. Its flexibility and proximity to real work allow it to fit naturally into busy days. To unlock that potential, though, we need to explore beyond length and delivery and focus on something more foundational: design awareness."
EDTECH@UTRGV's insight:
"Microlearning doesn’t ask us to do less as designers; it asks us to be more intentional."
The country needs a new social compact that emphasizes how workers can win.
EDTECH@UTRGV's insight:
"Although people will be shifted out of some work activities, many of their skills will remain essential. Workers will also be central in guiding and collaborating with AI, a change that is already redefining many job roles across the economy."
AI's value depends on the educators and leaders who wield it with intention and a commitment to equity, fairness, responsibility, and balance.
EDTECH@UTRGV's insight:
"AI amplifies educators rather than replacing them: In K–12 settings, AI is most effective when used to reduce administrative burden, support better decision-making, and free educators to focus more time on students and relationships."
This article presents a clear and optimistic argument that AI, when used intentionally, can enhance teaching, engagement, and equity in K–12 education rather than undermine it. I appreciate the authors’ consistent emphasis on AI as a tool that amplifies educators by reducing administrative burdens and strengthening human relationships, especially through improved communication with multilingual families. The concrete examples—such as translation tools increasing parent engagement and AI-supported data analysis helping identify at-risk students—make the case feel practical rather than theoretical. I also strongly agree with the focus on AI literacy for both teachers and students, particularly the idea of teaching critical skills like identifying bias and remixing AI output with human judgment from an early age. Overall, the article makes a compelling case that AI’s true value in K–12 lies not in automation for its own sake, but in advancing equity, supporting educators, and refocusing schools on the human-centered work that matters most.
This article offers a timely and necessary perspective on the ethical and legal complexities of AI use in schools, making it especially valuable for practitioners and school leaders. I found the discussion of algorithmic bias particularly compelling, as it clearly shows how overreliance on AI detection tools can unintentionally harm multilingual learners and reinforce inequities rather than protect academic integrity. The legal examples around deepfakes and student liability underscore that AI use is not just a technical issue, but one with serious real-world consequences that schools must proactively address through policy and education. I also appreciate the strong emphasis on a “human in the loop” approach, which reinforces the idea that professional judgment, not automation, should guide decisions affecting students.
Generative AI use by students took schools by storm, and that deluge only began a few years ago.
EDTECH@UTRGV's insight:
Sal Khan: "[W]e’re at the cusp of using AI for probably the biggest positive transformation that education has ever seen, and the way we’re going to do that is by giving every student on the planet an artificially intelligent but amazing personal tutor."
This article offers a sobering and insightful analysis of higher education at a moment of deep transformation, emphasizing how long-standing assumptions about institutional stability, autonomy, and purpose are rapidly eroding. I found the discussion of accountability and political pressure particularly compelling, as it shows how universities are increasingly judged by economic outcomes rather than educational mission, forcing leaders into defensive and often austerity-driven decisions. The author’s framing of AI as a shift from experimentation to infrastructural dependence resonated with me, especially the concern that governance, ethics, and academic judgment are lagging behind technological adoption. What stands out most is the warning that the true risk is not AI itself, but the quiet reshaping of authority, labor, and learning without intentional oversight. Overall, the article persuasively argues that higher education’s future depends on whether institutions choose thoughtful, values-driven transformation over reactive efficiency, a choice that will ultimately redefine trust, faculty roles, and the social contract of the academy.
What is Diagnostic Teaching? Diagnostic teaching is a step-by-step, intentional process for pinpointing exactly why a student is struggling.
EDTECH@UTRGV's insight:
"The big idea behind Diagnostic Teaching is to illuminate and remove barriers to student understanding. When students have problems, you need to be able to systematically identify and fix them."
We speak with Jenay Robert, senior researcher at Educause, about goals for generative AI in higher education, action steps for integrating AI effectively, and upcoming AI research.
EDTECH@UTRGV's insight:
Rhea Kelly and Jenay Robert discuss the 2025 Horizon Action Plan, highlight the shift from AI policy and outline concrete actions higher education institutions can take to build generative AI literacy over the next decade.
Artificial intelligence preparedness, classroom modernization, cybersecurity and esports will be front and center at TCEA 2026, running from Jan. 30-Feb. 4.
EDTECH@UTRGV's insight:
The UTRGV Educational Technology faculty be at the conference. Drop by Booth 1966 to learn more about this award winning program!
Time-to-competency is an operationally aligned metric by which to guage the success of an eLearning course.
EDTECH@UTRGV's insight:
"Although many metrics provide some superficial answers, they have not addressed the most significant business-related question: 'How quickly can learners carry out their tasks?' This is where time-to-competency or TTC adds significant value."
"The practical roadmap for introducing AI across grade levels, without sacrificing thinking, student ownership, or foundational skills."When we focus on skills before tools, AI becomes a support for learning instead of a shortcut around it."
EDTECH@UTRGV's insight:
"This free implementation guide gives you a clear, developmentally-grounded framework that prioritizes student thinking over technology adoption. You'll discover exactly which skills students need at each grade level before AI tools ever enter the picture."
Discover how to integrate AI into the daily workflow to reduce friction, strengthen judgment, and achieve measurable performance through practice and analytics.
EDTECH@UTRGV's insight:
"What does it really take for AI-powered learning to work inside the flow of work—and stay sustainable over time?"
The capabilities that make us human—communication, teamwork, and critical thinking—aren't "soft." Here's why we need to change the way we talk about those skills.
EDTECH@UTRGV's insight:
"AI is automating once-essential tasks, industries are evolving faster than education can keep pace, and the definition of career readiness has changed from 'ready to go on day one' to 'ready to adapt on day one.'”
As school districts embrace artificial intelligence to improve IT systems, a well-considered strategy can ensure a seamless transition.
EDTECH@UTRGV's insight:
"Artificial intelligence and machine learning have the potential to transform K–12 operations, increase efficiency and improve responsiveness. But AI and ML adoption in education is not without its challenges. Here are three obstacles that K–12 districts need to overcome."
The age of AI and Robots is here, you may be worried that robots will take your jobs. There are some jobs that have a low risk of being taken over by AI.
EDTECH@UTRGV's insight:
"The following 65 occupations were all determined to have a job automation risk probability of 0.0% based on the abilities, knowledge, skills, and activities that are required to perform the job well."
Today's students are future innovators in a landscape where powerful new tools of creation--AI--are sitting right in front of them.
EDTECH@UTRGV's insight:
"AI is about to pull the labor market in two directions at once: inward, as firms need fewer employees; and outward, as more individuals gain the tools to act like firms."
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"Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be"