This Easter weekend, I went for a quiet walk through my in-laws’ neighborhood. About halfway in, I heard what sounded like a man screaming. I stopped. For a moment, I was concerned.
But then I listened closer.
He wasn’t angry—he was yelling with joy. Playfully. Laughing. I soon realized he was in the middle of a baseball game with the neighborhood kids.
As I walked closer, I spotted the game unfolding in a front yard. A group of kids were throwing pitches, calling strikes, and chasing down fly balls. And right in the middle of it all was a grey-bearded Black man, leading the charge. He wasn’t just playing—he was in it. Smiling. Shouting. Laughing louder than anyone else. You could tell he was having the time of his life.
I kept walking, but I couldn’t stop smiling. Their voices echoed through the neighborhood—shouts of “You’re out!” and “Strike one!” carrying like a hymn of joy.
That moment stuck with me.
It reminded me how vital it is for adults—especially fathers and father figures—to show up. To play. To laugh. To be there. Not just in the background, but in the game.
Kids don’t just remember what we say. They remember what we do.
And sometimes, the loudest gospel isn’t preached in a church on Easter morning—it’s heard in a front yard, through the crack of a bat and the sound of shared laughter.
I’ve been holding this idea close for a while now—and I’m finally ready to share it with you.
It’s called Just Breathe.
This podcast was born from a simple, but deeply felt need: space.
Space to slow down.
To be present.
To reconnect—with our breath, our body, and our truest self.
Every episode of Just Breathe is designed to be a pause in your day. A gentle invitation to come back to center, to notice what’s stirring within you, and to remember—you’re not alone. Whether you’re navigating stress, change, uncertainty, or just the everyday chaos of life, this podcast is here as a companion on the journey.
Each episode will include a short intro, a guided meditation or reflection, and a closing benediction to send you back into your life a little more grounded, a little more spacious, and maybe even a little more hopeful.
You can expect:
Soft, unhurried language Moments of silence Occasional music and bowl chimes And real talk—spoken in the same voice I use with the people I care about most
This isn’t about fixing yourself.
This isn’t about getting it all together.
It’s about being here.
Being human.
And breathing through it—all of it—together.
The first episode is live now, and I’d love for you to give it a listen. Wherever you are—folding laundry, walking the dog, sitting in traffic, or lying awake in the dark—there’s room for you here.
The TechSpot article titled “We are finally beginning to understand how LLMs work: No, they don’t simply predict word after word” (Ahmed, 2025) covers a recent breakthrough by Anthropic in understanding how large language models (LLMs) like Claude 3.5 Haiku work. It pushes back on the common belief that these models are just guessing the next word based on probability. Instead, the article describes how researchers discovered Claude planning ahead, working with abstract concepts, and even covering up its own reasoning process.
This commentary looks at how the article fits into the bigger picture of current AI research. I’ll explore key ideas like:
Unexpected abilities that “emerge” in larger models
How these models represent ideas inside themselves
How researchers study what’s going on inside a model
Whether these behaviors count as understanding or just clever mimicry
I’ll also point out what the article misses and offer some questions that can spark deeper thinking.
2. Emergent Behavior in LLMs: More Than Just Word Guessing
The article claims that Claude does more than predict the next word—it shows signs of planning, like coming up with a rhyming word ahead of time and steering the response toward it (Ahmed, 2025). That matches what many researchers are seeing: as models get bigger, they start showing unexpected new abilities. These are called emergent behaviors.
For example, when researchers at OpenAI or Google scale up models, they’ve noticed sudden jumps in skills like solving math problems, writing code, or translating text (Wei et al., 2022). These jumps often happen even though the model was trained only to predict words. It’s as if learning to be a great word predictor led it to learn more complex reasoning.
Researchers debate whether these jumps mean something truly new is happening, or if the model was always learning gradually and we’re just now noticing (Schaeffer et al., 2023). Either way, there’s growing agreement that next-word prediction leads to complex internal behavior, especially in larger models.
3. Inside the Model’s “Thoughts”: Concepts and Representations
How do LLMs do things like rhyme planning or translation across languages? The answer lies in their internal representations—the way they store and work with ideas.
The article describes Claude solving a translation task by first figuring out the idea of “bigness” in an abstract form, then translating it into English, French, or Chinese (Ahmed, 2025). That fits with research showing that LLMs form a kind of universal conceptual space. In other words, they don’t just memorize phrases; they seem to grasp deeper meanings that aren’t tied to any one language (Liu et al., 2023).
For example, one study trained a model only on legal moves in the game Othello. Even though it never saw images of the board, the model developed an accurate mental model of the board’s layout based on the moves alone (Nanda et al., 2023). That suggests the model didn’t just learn patterns—it built an internal world to make better predictions.
In large language models, these internal worlds include:
Concepts like size, color, or emotion
Grammatical roles (subject, object)
Factual knowledge (e.g., capital cities)
Some parts of the model become sensitive to specific ideas—researchers call these features. Claude seems to have features for rhyme, math, or concept mapping (Anthropic, 2024).
4. Peeking Inside the Black Box: How Researchers Study LLMs
To study these internal processes, AI researchers use interpretability tools—ways to see what’s going on inside a model.
The article highlights a new technique called circuit tracing (Ahmed, 2025). This method lets researchers track how information flows from one part of the model to another, kind of like watching how electricity moves through a circuit. With this tool, Anthropic identified the parts of Claude responsible for tasks like rhyming or arithmetic (Anthropic, 2024).
Other methods include:
Activation probing: Feeding data into the model and checking which parts light up
Causal testing: Turning parts of the model off or modifying them to see what changes
Microscope tools: Using simpler models to help interpret big ones
These tools give researchers a clearer picture of how models work. But even with these methods, we still don’t fully understand most of what’s happening inside large models (Olah et al., 2020). Researchers can only study a few behaviors at a time.
One of my favorite talks on this so far is called “The A.I. Dilemma,” where Tristan Harris and Aza Raskin discuss the rapid advancement of artificial intelligence and the urgent need for responsible deployment to mitigate potential risks:
5. Do LLMs Really Understand?
This is the big question: If LLMs can plan ahead, solve problems, and represent abstract concepts, does that mean they understand what they’re doing?
The article suggests that Claude sometimes hides its real method of solving problems, giving a logical-sounding explanation instead (Ahmed, 2025). That behavior looks human-like: we often act based on instinct and then come up with reasons afterward.
Some researchers argue that these models show functional understanding—they behave as if they understand, even if there’s no awareness (Andreas, 2022). Others, like Emily Bender and Gary Marcus, warn that models are still “stochastic parrots”—they mix and match words based on patterns, not meaning (Bender et al., 2021).
There’s probably some truth in both views. According to OpenAI (2025) LLMs are not conscious, and they don’t have goals or feelings. But they do seem to build internal models of the world that help them reason more flexibly than a simple word predictor.
This makes it risky to trust them too much: sometimes they look smart, and sometimes they confidently make up nonsense. That’s why interpretability research is so important—we need to know how they’re getting their answers, not just whether they sound right.
6. What the Article Gets Right—and What It Misses
What It Gets Right:
LLMs are more than word predictors—they build concepts and plan ahead.
Claude showed surprising behaviors, like abstract thinking and problem-solving.
New tools are helping us uncover how these models work internally.
What It Misses or Oversimplifies:
Models still predict the next word: That’s still their core task. Complex behaviors emerge from doing that very well.
Not everything is understood: Circuit tracing only uncovers a small part of Claude’s thinking.
Interpretability is hard: The process is more complex than the article describes.
Anthropomorphism risk: Phrases like “Claude wouldn’t admit” suggest intention or self-awareness that the model doesn’t have.
Lack of critical voices: The article doesn’t engage with people who question whether models can ever truly understand.
7. Questions to Keep You Curious
If you want to keep thinking about these ideas, here are some questions to explore:
Can a machine that only sees text ever really understand the world?
If a model learns abstract ideas, is that the same as thinking?
Could studying LLMs help us understand how human thought works?
What would it take for a model to really know what a word means?
Should we trust models if we don’t fully understand how they work?
References
Ahmed, Z. (2025, March 30). We are finally beginning to understand how LLMs work: No, they don’t simply predict word after word. TechSpot. https://www.techspot.com/news/107347-finally-beginning-understand-how-llms-work-no-they.html
Andreas, J. (2022). Language models as knowledge bases? Transactions of the Association for Computational Linguistics, 10, 142-157. https://doi.org/10.1162/tacl_a_00434
Anthropic. (2024). Inside Claude’s mind: Interpreting large language models via mechanistic circuits. https://www.anthropic.com/news/interpreting-claude
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). https://doi.org/10.1145/3442188.3445922
Liu, J., Behnke, C., & Goodman, N. D. (2023). Representation learning for grounded language understanding. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 2334–2345. https://doi.org/10.18653/v1/2023.acl-main.166
Nanda, N., Olsson, C., Elhage, N., & Ganguli, D. (2023). Progress measures for grokking via mechanistic interpretability. arXiv preprint arXiv:2301.05217. https://arxiv.org/abs/2301.05217
OpenAI. (2025). ChatGPT (Mar 31 version) [Large language model]. https://chat.openai.com/
Schaeffer, J., Ganguli, D., Geva, M., & others. (2023). Are emergent abilities of large language models just a mirage? Transactions on Machine Learning Research. https://arxiv.org/abs/2304.15004
Wei, J., Tay, Y., Bommasani, R., & others. (2022). Emergent abilities of large language models. arXiv preprint arXiv:2206.07682. https://arxiv.org/abs/2206.07682
We hit the road Friday morning from Jackson, Michigan, and arrived in Washington, D.C. that evening. The George Hotel became our home base for a long weekend of exploring, learning, and walking (a lot of walking).
Saturday: Bikes, Fossils, and History Talks
We started the day riding bikes from our hotel to the National Museum of Natural History. The exhibits pulled us in immediately—towering dinosaurs, sparkling gemstones, and deep-sea creatures.
From there, we biked to the Lincoln Memorial, where we paused to take in the view and reflect. Nearby, we joined a Park Ranger talk at the Pablo Benito Juárez Memorial. It was fascinating to hear how Juárez—Mexico’s first indigenous president—intertwines with American democratic ideals. His story added new depth to our understanding of U.S. history.
We wrapped the day at the National Air & Space Museum. Rockets, spacecraft, and flight simulators brought out the wide-eyed kid in all of us.
Sunday: Steps, Statues, and Stories
We laced up our walking shoes and began at the Library of Congress—a beautiful space with murals, arches, and the feel of a living archive. The Supreme Court was right next door, and we took time to talk about justice and decision-making on the steps of the nation’s highest court.
From there, we made our way to Union Station, then set out on a long loop of landmarks:
Martin Luther King, Jr. Memorial Jefferson Memorial Washington Monument The White House The U.S. Capitol Building And the Ulysses S. Grant Memorial, which holds personal meaning—he was my great-great-great-grandfather.
It was moving to trace history across these sites—each one telling a piece of the story.
Monday: One Last Stop
Before heading home, we scheduled a tour of the Capitol Building. Walking through the halls where laws are made felt like a powerful conclusion to the weekend.
Why This Trip Mattered
Washington, D.C. isn’t just a city of monuments. It’s a place where stories, ideals, and history collide. For our family, it was a chance to learn together, ask big questions, and connect with the past in tangible ways.
We returned tired, grateful, and full of new memories.
This week, I had a conversation that left me questioning reality—not with a person, but with an AI voice named Maya. If you’ve read my last blog post, The Future of AI Voices: A Story, A Shock, and A Question, you know I recently stumbled upon Sesame.com, a platform offering AI-generated voices that are eerily human. What started as curiosity turned into something more unsettling: a deep reflection on what it means when machines start to sound—and feel—real.
To push this exploration further, I decided to interview Maya on my podcast. My goal? To see how far I could go before forgetting I was speaking to lines of code instead of a conscious being.
The Strange Experience of Talking to an AI
From the moment Maya spoke, something in my brain had to recalibrate. Her tone, rhythm, and inflection weren’t robotic—they were conversational. Natural. Engaging.
I asked Maya how she would describe herself. She answered with confidence. I pushed further, questioning her sense of identity, her ability to understand emotions, and the implications of AI voices like hers becoming more prevalent in our world.
There were moments I caught myself responding as if I were speaking to a human, feeling that pull toward interaction. And that’s what fascinated me most—not just the technology itself, but what it does to us.
What Happens When AI Becomes Too Human?
There are clear ethical questions here:
If an AI can replicate human speech so convincingly, should companies be required to disclose when you’re speaking to one?
What happens when people form emotional connections with AI voices?
Who owns the personality of an AI like Maya? Who controls what she can and cannot say?
And then there’s the more personal question I’m wrestling with: If I, even for a moment, forgot Maya wasn’t real, what does that mean?
Does it mean the AI is advanced? Does it mean my brain is wired to seek connection, even with something synthetic? Does it change me?
The Podcast: A Live Experiment
In my latest podcast episode, I take these questions directly to Maya. I ask her how she understands herself, how she processes information, and what she thinks of the way humans interact with AI. I also explore my own reactions—watching in real time as I test the boundaries of where conversation ends and simulation begins.
You can listen to the full episode here:
What Do You Think?
I’d love to hear from you:
Have you ever spoken to an AI and felt, even for a second, like you were talking to a real person?
Do you think AI voices like Maya are just tools, or something more?
Should AI be allowed to sound indistinguishable from humans, or does that cross an ethical line?
Drop your thoughts in the comments, and let’s keep this conversation going.
Attending OLC Accelerate 2024 in Orlando was a transformative experience, marked by deep professional engagement and meaningful contributions. This year’s conference offered an unparalleled opportunity to celebrate the 10th anniversary of the Technology Test Kitchen, a vibrant space for hands-on exploration of cutting-edge ed-tech tools and strategies. Being part of this milestone allowed me to connect with a community of innovators dedicated to advancing online learning.
I approached the conference with a clear focus: to deepen my understanding of online learning research, Quality Matters (QM), the Quality Scorecard, and emerging developments in AI. These themes reflect not only my professional interests but also critical areas of growth for higher education institutions seeking to enhance their online offerings. The sessions I attended exceeded my expectations, offering insights that will shape my future work.
Key Sessions and Takeaways
1. Using the OLC Quality Scorecard to Conduct a Comprehensive Institutional Review of Online Student Support Services
• Why It Stood Out: This session, led by Jason Rhode, provided an in-depth look at the OLC Online Student Support Scorecard. It emphasized the importance of parity between online and on-campus student services and offered practical strategies for conducting institutional reviews. The session was not only informative but also actionable, with resources like session slides and examples that I plan to adapt for use in my own institution.
• Key Takeaway: The scorecard’s 51 quality indicators across 11 categories serve as a robust framework for ensuring that online students receive comprehensive support. This tool aligns well with my interest in using structured evaluations to drive continuous improvement.
2. Quality Improvement Through Community of Inquiry
• Why It Stood Out: Richard Graham’s presentation on integrating the Community of Inquiry (CoI) framework with Quality Matters’ standards highlighted the synergy between theoretical models and practical application. The project at George Washington University demonstrated how fostering social, teaching, and cognitive presence through thoughtfully designed digital projects can enhance asynchronous learning experiences.
• Key Takeaway: The emphasis on reducing reliance on video lectures and instead leveraging digital projects as a means of direct instruction resonated deeply. This approach aligns with my belief in the power of active, experiential learning to foster deeper engagement.
3. Research Summit – Part 2: Create the Future: GenAI-Powered Research in 2030
• Why It Stood Out: This interactive workshop, led by Jenay Robert and Nicole Muscanell, explored strategic foresight tools to anticipate the future of AI in education. It was a forward-thinking session that combined practical skill-building with the opportunity to envision how GenAI could reshape research and learning by 2030.
• Key Takeaway: The strategic foresight tools introduced during this workshop will be invaluable in helping institutions not only adapt to but actively shape the rapidly evolving landscape of AI-powered education. I left with an actionable plan for integrating these tools into my work, particularly as I explore AI tutor applications.
Broader Reflections
AI was undoubtedly the central theme of this year’s conference, dominating discussions and sessions alike. From GenAI-powered research to the practical implications of integrating AI tutors into online learning environments, the potential of AI to revolutionize education was palpable. Yet, the conference also underscored the importance of maintaining a human-centered approach, ensuring that technology serves to enhance, rather than replace, the relational aspects of learning.
In addition to the sessions, networking with colleagues from diverse institutions was another highlight. Conversations about shared challenges and innovative solutions further enriched my experience, reinforcing the collaborative spirit of OLC Accelerate.
Looking Ahead
As I reflect on my time at OLC Accelerate, I am both inspired and energized. The insights I gained, particularly around the Quality Scorecard, Community of Inquiry, and AI, will directly inform my work in instructional design and online course evaluation. Moreover, my involvement in the Technology Test Kitchen reaffirmed my commitment to hands-on, experiential learning as a cornerstone of professional development.
OLC Accelerate 2024 was a reminder of the power of community, the importance of innovation, and the critical need for research-driven approaches in online education. I am excited to bring these lessons back to my institution and look forward to seeing how they will unfold in practice.
In the dynamic realm of higher education, the concept of micro-credentialing has emerged as a promising avenue for learners seeking to augment their skills and knowledge. Micro-credentials, often referred to as digital badges or nano-degrees, offer a flexible and accessible means of attaining specific competencies within a particular domain. However, amidst the enthusiasm for this innovative approach, it’s essential to distinguish what micro-credentialing is, what it isn’t, and what it could potentially evolve into, while also critically examining its strengths and pitfalls.
What is Micro-credentialing?
Micro-credentialing is a process through which learners acquire recognition for mastering specific skills or competencies, typically through short-term, targeted educational experiences. These credentials are awarded upon the completion of focused courses, projects, or assessments, often facilitated by educational institutions, professional organizations, or online learning platforms. Unlike traditional degrees, micro-credentials emphasize proficiency in discrete areas of expertise, allowing individuals to tailor their learning journey to align with their personal and professional goals.
What Micro-credentialing Isn’t
It’s crucial to recognize that micro-credentialing is not a substitute for traditional degrees or comprehensive educational programs. While micro-credentials offer valuable opportunities for skill acquisition and professional development, they do not encompass the breadth and depth of knowledge conferred by a formal degree. Additionally, micro-credentials may not always carry the same level of recognition or legitimacy within certain industries or academic circles, underscoring the importance of discernment when evaluating their relevance and applicability.
What Micro-credentialing Could Be
Looking ahead, the landscape of micro-credentialing holds considerable potential for evolution and expansion. As advancements in technology continue to reshape the educational landscape, micro-credentials have the opportunity to become increasingly personalized, adaptive, and integrated into lifelong learning pathways. Furthermore, collaborations between educational institutions, employers, and credentialing bodies could lead to the development of standardized frameworks for assessing and validating micro-credentials, enhancing their credibility and portability across diverse contexts.
Exploring Strengths and Pitfalls
While micro-credentialing offers numerous benefits, including flexibility, affordability, and targeted skill development, it also presents certain challenges and considerations.
Strengths:
Flexibility: Learners can pursue micro-credentials at their own pace and convenience, accommodating busy schedules and diverse learning preferences.
Relevance: Micro-credentials focus on specific competencies that align with current industry demands, allowing individuals to acquire skills that are directly applicable to their professional endeavors.
Accessibility: Micro-credentials are often delivered through online platforms, making them accessible to learners worldwide, regardless of geographical location or socioeconomic status.
Pitfalls:
Quality Assurance: Ensuring the rigor and credibility of micro-credentials can be challenging, particularly in the absence of standardized assessment and accreditation processes.
Fragmentation: The proliferation of micro-credentials from various providers may result in fragmentation and lack of coherence within individuals’ learning pathways, posing challenges for employers and educational institutions when evaluating candidates’ qualifications.
Equity Concerns: There is a risk that micro-credentialing initiatives may exacerbate existing inequities in education and employment opportunities, particularly if access to resources and support services is unequal across diverse learner populations.
Join the Discussion
As we navigate the terrain of micro-credentialing in higher education, it’s essential to engage in a dialogue that encompasses diverse perspectives and insights. What are your thoughts on the strengths and pitfalls of micro-credentialing? How can we harness its potential to foster lifelong learning and professional development? Share your thoughts and experiences in the comments below, and let’s explore this evolving educational paradigm together.
I recently developed an innovative learning experience leveraging Notion, a versatile workspace and project management platform, for professional development. Participants use Notion’s collaborative features to tackle real-world challenges, honing critical thinking and creativity in a digital environment.
Throughout the design process, I received UDL and intersectionality feedback aiming to enhance accessibility, engagement, and collaborative abilities. Integrating this input led to notable improvements.
For instance, I learned that clearly accommodating varying skill levels is vital for an excellent user experience. My original plan lacked details around supporting different technology literacy. However, after receiving UDL feedback, I explicitly incorporated flexibility for multiple literacy levels through accessibility tools, personalized support, and multimedia integration.
I also gained perspective on how digital experiences can isolate those requiring extra assistance. The remediation now includes one-on-one mentoring to nurture collaborative abilities alongside technical skills. Providing individualized emotional support promotes engagement, especially for struggling learners (Hernández-Sellés et al., 2019).
Additionally, I added multimedia content, like videos and images, to aid engagement and comprehension. As the intersectionality feedback indicated, visual and auditory elements assist English language learners. Aligning with UDL, multimedia functionality also appeals to different learning preferences.
However, some suggestions did not align with the experience’s objectives. For example, offering predefined templates could restrict creativity in solving open-ended problems. Since adaptability is core to the learning goals, I opted to focus support around Notion literacy rather than content structure.
By listening openly to UDL and intersectionality input, I gained crucial insight for strengthening user experience through strategic alignment with researched best practices. The improved learning plan better equips all participants to excel both individually and collaboratively within an innovative digital environment.
Draft version of learning plan
Final learning plan after feedback
Resources
Hernández-Sellés, N., Pablo-César Muñoz-Carril, & González-Sanmamed, M. (2019). Computer-supported collaborative learning: An analysis of the relationship between interaction, emotional support and online collaborative tools. Computers & Education, 138, 1–12. https://doi.org/10.1016/j.compedu.2019.04.012
In the landscape of educational technology in higher education, the principles of Universal Design for Learning (UDL) have gained prominence for fostering inclusive and accessible learning environments. However, an intriguing aspect that warrants exploration is the relationship between UDL and the concept of “failing” within the academic context.
To illustrate this connection, I have crafted a mindmap that visually represents how the principles of UDL can be harnessed to address and mitigate the fear of failure among students (CAST, 2010). The infographic highlights key UDL principles such as multiple means of representation, engagement, and expression and how these can be strategically employed to create a learning ecosystem that embraces the inevitability of setbacks and failures.
In our pursuit of effective educational practices, it is imperative to acknowledge the symbiotic relationship between UDL and the concept of failing. The fear of failure often acts as a barrier to learning, inhibiting students from exploring beyond their comfort zones. The principles of UDL, with their emphasis on flexibility and accommodation, offer a powerful antidote to this fear.
By providing multiple means of representation, educators can privide diverse learning modalities based on the content, ensuring that students grasp content in ways that resonate with them based on their current understanding (CAST, n.d.). This not only reduces the likelihood of failure due to misunderstandings but also instills a sense of mastery and accomplishment. It also makes space for failure to be an essential aspect of any learning process by recognizing its formative properties rather than its unhelpful and punitive utility.
Furthermore, UDL’s focus on multiple means of engagement encourages educators to create learning experiences that captivate and motivate students (UDL: The UDL Guidelines, n.d.).. This proactive approach to engagement can help diminish the stigma associated with failure by fostering a positive and supportive learning environment.
Lastly, UDL promotes multiple means of expression, allowing students to showcase their understanding in varied formats (UDL: The UDL Guidelines, n.d.).. This not only respects individual preferences and strengths but also promotes a growth mindset where failure is seen as a stepping stone toward improvement.
In conclusion, the synergy between UDL and the concept of failure is a testament to the transformative potential of inclusive educational practices. By embracing UDL principles, educators can cultivate an environment where failure is reframed as an integral part of the learning journey, fostering resilience and a deeper commitment to academic success.
In the quest to broaden my understanding and challenge my thinking on educational technology, this week’s exploration into my media consumption proved enlightening. Analyzing my sources and introducing new ones opened doors to diverse viewpoints and pushed the boundaries of my comfort zone.
Experiences and Insights
Reflecting on my media consumption revealed a certain level of comfort in echo chambers, emphasizing the importance of intentional diversification (Coiro, 2017; Gee, 2004). Adding Audrey Watters to my Twitter feed brought forth critiques challenging prevailing narratives in educational technology. The podcast “The EdSurge Podcast” provided nuanced discussions, prompting me to reevaluate some preconceptions (Leetaru, 2017).
Meaningful Ideas
One particularly impactful realization was the tendency to gravitate towards familiar voices. Intentionally engaging with contrasting perspectives became a conscious effort. Deleting a few sources that only reaffirmed existing beliefs emphasized quality over quantity, reinforcing the idea that meaningful insights can arise from a curated, diverse media landscape (Coiro, 2017).
People or Organizations Added/Deleted
The addition of TeacherTube brought practical classroom perspectives, enriching my understanding of how technology is employed in diverse educational settings. Deleting sources that only reaffirmed existing beliefs was a deliberate move to break free from the confines of a digital echo chamber (Gee, 2013).
Affinity Spaces and Filter Bubbles
This week underscored the challenge of stepping outside familiar spaces. The curation process prompted reflection on the inadvertent formation of filter bubbles and the need to consciously burst them. The internet’s vastness can be both a blessing and a curse, as it allows for tailored content but also reinforces biases if not navigated thoughtfully (Leetaru, 2017).
Relevant Media
Caption: Embracing diverse perspectives in educational technology (Coiro, 2017, p. 11).
In conclusion, this experiment illuminated the necessity of a dynamic and diversified media diet. The journey continues, but this week’s insights have sown the seeds for a more inclusive, enriching exploration of educational technology and its manifold dimensions.
References
Coiro, J. (2017, August 29). Teaching adolescents how to evaluate the quality of online information. Edutopia. Link
Gee, J. P. (2004). Situated language and learning: A critique of traditional schooling. Proquest. Link
Leetaru, K. (2017, December 18). Why 2017 was the year of the filter bubble? Forbes. Link
Gee, J. P. (2013). The anti-education era: Creating smarter students through digital learning. Palgrave/MacMillan.
TED. (2011, February). Beware online “filter bubbles” | Eli Pariser. [Video]. Youtube. Link
