Alex's Blog Posts

When I moved to Edmonton about three years ago, I found myself drawn to local Nigerian and African communities. Through various community events and gatherings, I noticed something that deeply resonated with me as an educator and language instructor: many adults struggled to speak their local languages fluently, and parents often expressed a desire for their teenage children to learn languages like Yoruba, Hausa, and Igbo. These interactions made me reflect on the cultural and linguistic gaps that emerge in diaspora contexts and how technology might offer a bridge.

My background as an ESL instructor and counselor, combined with over a decade of experience in education, has given me insight into how adults approach learning, particularly language learning. During this MSc program, there was a time I explored the concept of gamification, I became fascinated with how it could transform adult learning experiences. That curiosity has evolved into my current research focus: developing AI-powered gamified platforms that enhance conversational fluency for adult learners. I believe that by integrating artificial intelligence with game-based design, we can make learning not only more effective but also more engaging and culturally relevant.

Thinking about how to disseminate my research, I have developed a threefold approach, each addressing a different dimension of impact; academic, professional, and commercial.

1. Academic Dissemination:
I plan to publish my research findings in academic journals focused on educational technology, applied linguistics, and adult learning. Journals such as Computers & Education or Language Learning & Technology would be ideal platforms to share the theoretical and empirical aspects of my work. Publishing academically is important to me because it allows my findings to contribute to the broader scholarly conversation on how AI and gamification intersect with adult education. It will also provide a space for peer review, ensuring the research holds academic rigor and relevance.

2. Professional Dissemination:
Beyond academics, I aim to engage with the professional teaching community through conference presentations, workshops, and teacher training sessions. As a language instructor and founder of a teaching firm, I understand the importance of connecting theory to classroom practice. Through professional development workshops, I can share practical strategies for integrating gamified tools into language instruction, especially for educators working with adult or multilingual learners. This form of dissemination would also open opportunities for collaboration with other teachers, educational technologists, and institutions seeking to implement innovative teaching methods.

3. Commercial and Community-Based Dissemination:
I intend to create a minimum viable product (MVP), a prototype of the gamified language learning platform. This web or mobile application would serve as a tangible demonstration of my research outcomes. The platform would initially target adult learners within Nigerian and African diaspora communities, providing an engaging space for learning indigenous languages such as Yoruba, Hausa, and Igbo. However, the long-term goal is to extend its scope to other languages and learning contexts. By integrating AI, the tool could personalize feedback and adapt learning pathways based on user performance, while gamification elements like challenges, levels, and rewards would sustain motivation.

While platforms like Duolingo and similar products already exist, my intention is to create a solution that strikes a balance between educational value and cultural preservation, rather than leaning fully into entertainment. I envision this prototype as both a community resource and a proof of concept that could attract partnerships or further development opportunities. Disseminating through a working product not only allows the research to have practical impact but also helps validate its real-world applicability.

I feel that using my threefold approach will be an extension of my commitment to education and innovation. Sharing this work across academic, professional, and community channels ensures that it reaches diverse audiences; researchers, educators, and learners alike.

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3 thoughts/ideas

1. Presence builds trust. In week 1 I said that connection feels different online. During this course, I saw why. Learners read our presence in small, steady signs: quick check-ins, clear goals, warm tone, and fast replies. These signals tell people it is safe to speak and safe to not know yet. I stopped guessing that students “just know.” I now ask simple guiding questions and invite pause and reflection. Work on social and teaching presence backs this up (Lowenthal, n.d.; Hufford, 2014).
2. Inclusion must be built in from the start. I still hold that flexibility and mixed modes help. But flexibility alone can hide quiet voices. My job is to make it safe to ask, safe to try, and safe to fail. That means clear paths to take part, plain language, and a marking guide that shows what “good” looks like. It also means more than one way to show learning. In my exams work, that could be varied prompt forms, a fair time window, and clear rubrics. Research on teachers’ inclusive practice points to design for all, not last-minute fixes (Finkelstein et al., 2021; JISC, n.d.).
3. Simple rituals grow a group. Our team used talking circles. One voice at a time. No rush. No blame. This small practice did real work: it let people share, hear, and be heard. It turned a set of names on a screen into a group that cared. This matches reports on circle work in online teaching, when done with care for its roots. Paired with clear norms and short “pulse checks,” circles help keep a course alive (Schwartz et al., 2020; Forj, 2024).

2 questions

1. Exams and inclusion. In my role as an exams facilitator, how do I design tests that are fair and also inclusive? Which steps give the best gains with low extra load—varied prompt types, plain-English rubrics, a time window, or a short “how to take this test” guide? What mix keeps standards high and bias low (Finkelstein et al., 2021)?
2. Trust at large scale. What works best to build trust in big, at-different-times groups? Ideas to test: short welcome paths, a clear “how we work here,” rotating peer roles, and set points for peer review. How do we track trust over time in ways that are light and honest (Forj, 2024; JISC, n.d.)?

1 metaphor
I still see online facilitation as a summer campfire on the internet. The host does not just light the fire. We also set the circle, name the shared rules, and pass a talking piece. The heat is shared. Stories rise when the space feels safe. Without a planned circle, the fire burns but people hold back (Lowenthal, n.d.; Talking circles… 2022).

References
Finkelstein, S., Sharma, U., & Furlonger, B. (2021). International Journal of Inclusive Education, 25(6), 735–762.
Forj. (2024, May 16). How to revive a stagnant online community.
Hufford, D. (2014). Presence in the classroom. New Directions for Teaching and Learning, (140), 11–21.
JISC. (n.d.). Building digital capabilities framework.
Lowenthal, P. (n.d.). Talking Social Presence [Video]. YouTube.
Talking circles as Indigenous pedagogy in online learning (2022). ScienceDirect.
Schwartz, M., et al. (2020). Digital citizenship toolkit. Toronto Metropolitan University.

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In my facilitation context (higher education courses delivered in blended and online formats) I rely on the Community of Inquiry (CoI) framework to guide how I design and support learning. The model highlights three dimensions of presence: teaching, social, and cognitive.

My representation focuses on practical strategies that I can apply directly in digital classrooms.

Teaching Presence is central because learners need clarity and guidance in online spaces. I emphasize setting expectations through weekly announcements and clear rubrics. I also provide timely, personalized feedback, which helps learners stay on track. Finally, I model how to use digital tools such as breakout rooms or shared documents so students can engage confidently. These align with Garrison, Anderson, and Archer’s emphasis on purposeful design and facilitation.

Social Presence ensures learners feel connected to each other, not just to the instructor. I begin courses with icebreakers and structured introductions, which help humanize the learning environment. I design discussion prompts that encourage learners to reply to peers rather than posting in isolation. In addition, I create informal spaces, like a “virtual café,” where students can connect without pressure. These strategies echo research on the role of social presence in building trust and sustaining engagement.

Cognitive Presence develops when learners move from surface participation to deeper integration of ideas. I support this by asking open-ended questions that invite multiple perspectives. I scaffold prompts that guide learners through exploration and integration stages, and I design assignments that connect theory to practice in students’ professional or personal contexts. These choices reflect the CoI model’s focus on sustained critical inquiry.

What stands out most in my representation is how the three presences are interdependent. Teaching presence creates structure, social presence builds community, and cognitive presence drives meaning-making. In practice, no single presence can flourish without the others. By intentionally designing strategies across all three, I can create richer learning experiences for my students.

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3 Initial Thoughts, Ideas, or Feelings

1. This might be a general and cliché thought but Connection feels different online. Building trust and engagement in digital spaces seems less intentional and structured (in my opinion) compared to face-to-face facilitation which is contrary to what “Boettcher, 2013” pointed out. Or at least I could say, it is subjective to people that are being thought.
2. Flexibility is important. One thing that is true is that digital environments allow for asynchronous and synchronous interactions, but this flexibility allow for challenges in maintaining momentum (University of Wisconsin–Madison, 2021).
3. The facilitator’s role is evolving. Instead of being the primary source of knowledge, facilitators act as guides, community-builders, and curators of resources (Rapanta et al., 2020). This evolution can be considered a positive one because if students are guided in the right way they come to conclusions by engaging their creative sides.

2 Questions I Have

1. How can digital facilitators balance structure with learner autonomy, ensuring both direction and flexibility?
2. What strategies best support building a sense of community when learners may never meet in person?

1 Metaphor

Digital facilitation feels like hosting a summer campfire on the internet—the facilitator sets the fire (content and structure), but it is the participants who bring the stories, energy, and warmth that make it meaningful.

References

Boettcher, J. V. (2013). Ten best practices for teaching online. Designing for Learning. http://designingforlearning.info/writing/ten-best-practices-for-teaching-online/

University of Wisconsin–Madison. (2021). Facilitation strategies for online discussions. https://kb.wisc.edu/instructional-resources/page.php?id=121264

Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. (2020). Online university teaching during and after the Covid-19 crisis: Refocusing teacher presence and learning activity. Open Praxis, 12(4), 589–603. https://openpraxis.org/articles/10.5944/openpraxis.10.1.721

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Conducting a critical inquiry into YouTube-based ESL learning, using English with Emma as a central case, deeply transformed how I understand learner autonomy, digital pedagogy, and platform power. What began as an exploration of informal language learning evolved into a reflection on the invisible forces (algorithmic sorting, commercial logic, and sociotechnical systems) that structure what learners access and how they navigate learning environments.

Initially, I viewed YouTube as a democratic platform: open, accessible, and learner-driven. However, through examining recommendation systems and platform design, it became clear that autonomy on YouTube is constrained by algorithmic architecture. As Jeong, Oh, and Kim (2022) argue, algorithms are not neutral, they reflect platform priorities and reinforce patterns of visibility based on engagement, not educational merit. This means content that retains attention, not necessarily content that scaffolds learning, is most often promoted.

English with Emma provides a useful example. Emma’s structured, grammar-focused videos are pedagogically sound, featuring captions, clear visuals, and consistent pacing (Brook, 2011). Yet, when comparing her channel with Speak English with Vanessa (which relies on affective engagement and storytelling) and BBC Learning English, with its institutional SEO and polished production, it became clear that visibility is not solely a result of instructional quality. Algorithmic metrics like watch time and click-through rates favor certain styles over others, skewing what learners see (Buffer, 2023; SocialBlade, 2025).

This realization reshaped my understanding of learner behavior and progression. As Yu, Henderson, and Dang (2024) point out, many learners misinterpret algorithmically recommended content as pedagogically sequenced. In practice, this can trap learners in repetitive content loops, particularly if they lack platform literacy. My own experimentation with multiple YouTube accounts confirmed this. Despite entering identical search terms, my recommended videos diverged quickly depending on prior views and user location, validating what New America (2020) describes as “algorithmic bubbles.”

What shifted most profoundly was my recognition that ESL educators must not only teach language skills but also foster critical algorithm literacy. Learners need tools to interrogate why they are shown certain content and how to search intentionally. Curriculum models like DAILy’s “Redesign YouTube” (2023) offer engaging ways to teach algorithmic systems in language classrooms. Similarly, Barreto-Baca (2022) provides beginner-friendly resources that explain algorithmic bias through ELL-accessible language.

This process also made me reflect on broader equity and inclusion. Most algorithmically favored content is created by white, native-English speakers, reinforcing a narrow linguistic norm. Voices from the Global South, multilingual teachers, and regional dialects are less visible, despite their importance to learners worldwide.

Moving forward, I see my role evolving, from content designer to learning environment mediator. Educators in digital spaces must help learners both engage with and critique the platforms that shape their access to knowledge.

As Selwyn (2010) reminds us, technology is never just a tool, it is a site of power. And if we hope to empower our learners, we must first understand the systems shaping their learning.

References

Barreto-Baca, N. (2022). Introduction to bias for ELLs [Lesson plan]. Developing AI Literacy (DAILy) Curriculum. Everyday AI.

Brook, C. A. (2011). The affordances of YouTube for language learning and teaching. TESOL Working Paper Series, 9(1–2), 37–56. https://www.hpu.edu/research-publications/tesol-working-papers/2011/9_1-2_Brook.pdf

Buffer. (2023). A 2025 guide to the YouTube algorithm: Everything you need to know to boost your content. Buffer.

DAILy Project. (2023). 3.5 Redesign YouTube Lesson Plan. Developing AI Literacy.

Jeong, H.-S., Oh, Y.-J., & Kim, A. (2022). Critical algorithm literacy education in the age of digital platforms. In Learning to Live with Datafication (pp. 153–168). Routledge. https://doi.org/10.4324/9781003136842-9 

New America Foundation. (2020, March 26). How recommendation algorithms shape your online experience.

Selwyn, N. (2010). Looking beyond learning: Notes towards a critical history of educational technology. Journal of Computer Assisted Learning, 26(1), 65–73. https://doi.org/10.1111/j.1365-2729.2009.00338.x 

SocialBlade. (2025). YouTube analytics: English with Emma, Speak English with Vanessa, BBC Learning English. https://socialblade.com/

Yu, A. S., Henderson, M., & Dang, T. K. A. (2024). Challenges for being self-directed in content and language integrated learning with instructional videos. In T. Cochrane et al. (Eds.), ASCILITE 2024 Conference Proceedings: Navigating the Terrain (pp. 147–155). Australasian Society for Computers in Learning in Tertiary Education. https://doi.org/10.14742/apubs.2024.1224 

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As part of our team inquiry into YouTube as an educational technology, I’ve been exploring the English with Emma channel and how algorithm-driven content delivery impacts learner autonomy and inclusivity.

My focus is on a dual critical issue: (1) the role of YouTube’s algorithm as an educational gatekeeper, and (2) how sociolinguistic characteristics (such as accent, race, gender, and perceived nativeness) influence which English instructors gain visibility and credibility on the platform.

Through my experience so far, I’ve noticed how YouTube’s algorithm tailors content recommendations after only a few video views. While this personalization offers convenience, it also narrows the learner’s exposure to diverse linguistic models. For ESL learners seeking to develop well-rounded communicative competence, this presents a serious pedagogical limitation. Rather than scaffolding a progressive learning journey, the algorithm seems to optimize for retention and watch time (not language development) (Tufekci, 2015).

A striking pattern is the visibility of “native” English speakers who fit into a very particular profile (usually white, Western, and female) many of whom teach with Standard American or British accents. This reinforces what Jennifer Brook (2011) calls “native-speakerism,” a bias that privileges certain forms of English and sidelines others. For learners across different cultural and geographic contexts, this presents questions of linguistic representation and psychological ownership of English.

I believe this issue is important because it connects critical discussions around digital equity, learner agency, and the sociopolitical construction of language authority. If educational technologies like YouTube are shaping the choices, voices, and norms learners engage with (often invisibly) then we need to better understand what pedagogical power is embedded in algorithmic systems. As Selwyn (2010) argues, critical inquiry must go beyond surface-level affordances and interrogate the deeper social and political forces at play.

I invite others to consider:

• In what ways do learners lose or gain autonomy when their ESL content is curated by a platform like YouTube?
• How might content creators subvert or challenge dominant norms to promote linguistic diversity?
• Are there examples of YouTube channels that actively resist algorithmic homogenization and amplify underrepresented accents or identities?

Your thoughts and suggestions, (especially on tools, theories, or frameworks to help explore this further) would be incredibly valuable as I continue to develop my individual learning plan and final paper.

References
Brook, J. (2011). The affordances of YouTube for language learning and teaching. HPU TESOL Working Paper Series, 9(1-2), 37–56.
Selwyn, N. (2010). Looking beyond learning: Notes towards the critical study of educational technology. Journal of Computer Assisted Learning, 26(1), 65–73.
Tufekci, Z. (2015). Algorithmic gatekeeping. Scientific American, 313(5), 78–81.

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As an educator with over 11 years of experience in diverse contexts, my journey in teaching has always been about finding innovative ways to meet learners’ needs. From teaching Cambridge International Curriculum in Nigeria to establishing my ESL firm and collaborating with international educators, I’ve seen firsthand how rapidly evolving learner preferences challenge traditional approaches to instruction. My recent exploration of design thinking models has deepened my understanding of these challenges and provided a foundation to craft guiding principles for my future instructional designs.

The Pecha Kucha presentation highlighted a critical issue in contemporary learning environments: learners often seek quick results with minimal effort, sometimes bypassing foundational principles. This creates a significant challenge for educators—how can we design learning experiences that not only engage but also ensure depth and retention? Below are the design principles I’ve developed to address this challenge, informed by my experiences, readings, and a commitment to learner-centered education.

Design Principles to Guide My Practice

1. Prioritize Foundational Learning Foundational knowledge is essential for meaningful learning. Drawing from Ebbinghaus’ Forgetting Curve and the Spacing Effect, I aim to design instructional materials that integrate repetition and reinforcement of foundational concepts. This ensures learners build a strong base before moving to advanced topics.
2. Incorporate Gamification and Interactivity Gamified elements, as suggested in the Pecha Kucha, can make learning engaging and enjoyable. By embedding interactive tools and games into lessons, I aim to make repetitive and foundational tasks more appealing, reducing the reliance on shortcuts while fostering active participation.
3. Design for Scaffolding and Gradual Progression Learning should be structured to provide manageable steps, ensuring each stage builds on the previous one. Drawing from Crichton and Carter’s (2017) toolkit, I will integrate scaffolding strategies that guide learners progressively from basic to advanced levels, balancing quick wins with deeper engagement.
4. Leverage Technology for Personalized Learning Technology offers opportunities for adaptive learning tailored to individual needs. Inspired by the flexibility of tools I used during my ESL teaching journey, I will incorporate platforms that adjust content delivery based on learner progress and preferences.
5. Emphasize Collaborative and Social Learning Social constructionist theories emphasize that humans learn best in community. By integrating collaborative projects, peer reviews, and group discussions, I aim to create a learning environment that encourages collective growth and shared understanding.
6. Embed Reflective Practices Reflection is vital for deep learning. I will design activities that prompt learners to pause, assess their understanding, and connect new knowledge to prior experiences, fostering metacognition and long-term retention.
7. Promote Lifelong Learning Mindsets In a world driven by instant gratification, fostering patience and perseverance is critical. I will integrate discussions and activities that highlight the value of effort, persistence, and delayed gratification in achieving meaningful outcomes.
8. Ensure Accessibility and Inclusivity Inspired by my work with learners from diverse cultural and linguistic backgrounds, I aim to design content that is accessible to all, ensuring inclusivity in language, format, and delivery.
9. Measure and Iterate for Continuous Improvement Learning environments should evolve based on feedback and data. Drawing from Freixanet et al. (2020), I will implement mechanisms to gather learner feedback, assess effectiveness, and refine instructional strategies to meet changing needs.
10. Balance Rigor with Flexibility While maintaining high academic standards, I will create flexible pathways for learners to engage with material at their own pace. This approach ensures that learners meet learning outcomes without feeling overwhelmed.

Contextual Application

These principles are particularly relevant to my work in developing tools for language learning in fast-paced environments. For instance, when designing ESL courses, gamified tools can make grammar practice engaging, while scaffolding ensures that learners progress systematically. Reflective practices and social learning can help learners connect language to their cultural and professional contexts, fostering deeper connections.

By implementing these principles, I aim to bridge the gap between learners’ desire for immediate results and the need for sustained effort. Ultimately, my goal is to create meaningful, effective learning experiences that empower learners to succeed in their personal and professional endeavors.

References

• Baker III, F. W., & Moukhliss, S. (2020). Concretising Design Thinking: A Content Analysis of Systematic and Extended Literature Reviews on Design Thinking and Human‐Centred Design. Review of Education, 8(1), 305-333.
• Crichton, S., & Carter, D. (2017). Taking Making into Classrooms Toolkit. Open School/ITA.
• Freixanet, J., Rialp, A., & Churakova, I. (2020). How do innovation, internationalization, and organizational learning interact and co-evolve in small firms? A complex systems approach. Journal of Small Business Management, 58(5), 1030–1063. https://doi.org/10.1111/jsbm.12510
• Goldman, S., et al. (2012). Assessing d.learning: Capturing the journey of becoming a design thinker. In H. Plattner, C. Meinel, & L. Leifer (Eds.), Design Thinking Research: Understanding Innovation (pp. 13-33). Berlin: Springer.
• Gray, C. (2020). Markers of Quality in Design Precedent. International Journal of Designs for Learning, 11(3), 1-12. https://doi.org/10.14434/ijdl.v11i3.31193

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As a teacher, I have always been on the lookout for frameworks that will enhance the learning experience of my students. The readings of this week exposed me to a number of ID models that shaped my thought on how best to effectively and efficiently build an environment for learning. Given my work in ESL teaching and language learning, these models offer inestimable insights into improving the ways we design courses that are practical and centered on the learner.

Instructional Design Models and Their Relevance

The readings underlined a wide range of instructional design models, each with unique features and strengths. One model that resonated with me is ADDIE, or Analysis, Design, Development, Implementation, Evaluation, one of the most widely used frameworks in instructional design. The iterative nature of ADDIE is particularly valuable in language learning contexts, where ongoing assessment and adaptation are crucial. Because language learners have different backgrounds and learning needs, the ability to continuously revise and improve course materials and delivery based on ongoing feedback is important (Dousay, 2017). In my own practice, I have found that a structured approach, such as ADDIE, helps me to tailor language lessons to the specific needs of my learners, whether they are beginners or advanced students.

Another model that really impressed me with its relevance is Universal Design for Learning (UDL), which calls for creating inclusive learning environments by offering multiple means of engagement, representation, and expression. According to Takacs et al. (2021), UDL fully corresponds to my aim of making language learning accessible to a wide range of students with different learning preferences, abilities, and cultural backgrounds. For example, in designing language learning tools, I often incorporate a variety of resources, such as interactive videos, audio recordings, and text-based materials, to cater to different learning styles. UDL also promotes learner autonomy, which is essential in a fast-paced and dynamic field like language learning, where students often need to take ownership of their learning (Takacs et al., 2021).

While UDL provides a broad framework, Heaster-Ekholm’s 2020 analysis of popular instructional design models really helped me understand the theoretical underpinnings of what makes those models effective. Specifically, Heaster-Ekholm pointed out that, at the very core of instructional design models, are both cognitive and constructivist theories of learning. That is particularly important in language teaching, which requires learners to construct meaning through interaction and practice. By incorporating constructivist principles into my language teaching, I aim to create environments where learners engage in meaningful, context-rich tasks that encourage problem-solving and critical thinking (Heaster-Ekholm, 2020).

Cultural Considerations and Contextual Factors

The second important point that came across the readings is the cultural understanding or relevance of the instructional designs. Parchoma et al. (2020) investigate how instructional and learning design practices might differ across culture and context; therefore, effective instructional design should accommodate diversity in the learner population. In my practical experience in teaching ESL to a multicultural group of students, this insight really resonated with me. Language learners are from diverse cultural backgrounds, and what works in one group may not work in another. For instance, in developing online language courses, I have to consider the varying expectations and learning behaviors of students from different cultural contexts. Whereas some learners will do better with a highly structured and directive approach, others might prefer one that is more flexible and collaborative in style (Parchoma et al., 2020). Therefore, being aware of such cultural differences will really help in developing learning experiences that are not only effective but also respectful and inclusive toward the backgrounds of the learners.

Integrating ID Models into My Practice

I intend to use these models in my work at the language learning firm, integrating these elements into our course design. The ADDIE model will, therefore, be a guiding framework for developing, implementing, and refining language courses. Through continuous needs assessment and revision of course materials, it is possible to continuously rework the course products with feedback from learners so that the offerings remain relevant and effective. Additionally, UDL will be fully incorporated into my future course design, especially in online learning environments. By offering multiple means for students to access content and show their understanding, I will make it possible to address differences in learning preference and needs and maximize learning (Takacs et al., 2021).

I also see the value in taking a more constructivist approach, as highlighted by Heaster-Ekholm (2020). Language learning is not only about memorizing vocabulary or grammar rules; it is about using the language in real, natural contexts. I plan to design tasks that encourage learners to practice the language in practical, everyday situations, such as role-playing conversations or solving real-world problems. This aligns with the belief that learners must actively construct their own understanding of the language through experience and interaction (Heaster-Ekholm, 2020).

Conclusion

Reflecting on the instructional design models covered in this week’s readings, I feel that there is a great potential to improve my language teaching practice. By combining elements of ADDIE, UDL, and constructivist learning principles, I can design courses that not only meet the diverse needs of my learners but also engage them in meaningful, real-world tasks. These readings definitely reinforce my view that ID should be flexible, inclusive, and responsive to the distinctive needs of learners, especially when working within a field as organic as language learning. I look forward to continuing to apply these frameworks in my work to provide more effective, engaging, and culturally responsive language learning environments for my students.

References

Dousay, T. A. (2017). Chapter 22. Instructional Design Models. In R. West (Ed.), Foundations of Learning and Instructional Design Technology (1st ed.).

Heaster-Ekholm, K. L. (2020). Popular Instructional Design Models: Their Theoretical Roots and Cultural Considerations. International Journal of Education and Development Using Information and Communication Technology, 16(3), 50–65.

Parchoma, G., Koole, M., Morrison, D., Nelson, D., & Dreaver-Charles, K. (2020). Designing for learning in the Yellow House: A comparison of instructional and learning design origins and practices. Higher Education Research & Development, 39(5), 997–1012.

Takacs, S., Zhang, J., Lee, H., Truong, L., & Smulders, D. (2021). A comprehensive guide to applying Universal Design for Learning.

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Fellow grad student,

Stephen and I teamed up to explore a persistent learning challenge using a design-thinking framework. We both recognized a shared struggle in our classrooms: learners often prioritize quick, effortless solutions over deeper, more meaningful engagement with the material.

To gain a better understanding, we interviewed each other to empathize with the unique ways this challenge manifests in our respective educational settings. We delved into the nuances of the problem, reflecting on how students navigate and sometimes avoid foundational learning.

We presented our findings through a PechaKucha presentation.

I’d love to hear your thoughts on our exploration—feel free to share!

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Introduction
Much of the terrain for education should be reshaped by 2030 with these new opportunities available for more personalized learning and greater administrative efficiency. However, these advances necessarily come with large ethical and social problems that could bring damage, to be handled with extreme care. The following essay investigates the ambivalence of AI in educational provisions and discusses promises and pitfalls in the light of critical reasoning derived from sources available in the literature. Analyzing these aspects, this essay will suggest mechanisms by which the benefits of AI may be potentially exploited whilst minimizing risks that come with it. The debate shall critically examine how AI might affect pedagogical approaches, educational equity, and the ethical necessities by educational institutions in order to ensure that AI technologies deployed have a positive contribution to the future of education.

Background: The Changing Role of AI in Education
The integration of AI in educational environments has developed from simple task automation to complex and interactive systems that enhance learning and operational efficiency. Indeed, researchers such as Macgilchrist et al. (2020) explained how the development for AI systems has been moving from just administrating support toward direct operation within pedagogical processes. Such systems are developed to respond to and adapt to the needs of individual learners, with the potential to transform the traditional paradigm in education. With greater development in AI, their use in education has the potential to offer more personalized and accessible learning experiences. At the same time, this development could bring about challenges of ethical management of student data and potential reinforcement of existing educational disparities due to AI. To help anticipate these trends and prepare for their implications, there is a need to understand the historical and ongoing development of applications of AI in education.

Promises of AI in Education
1. Personalized Learning
The biggest promise for the sector of education is the ability of AI for personalization of learning experiences. Through adaptive learning technologies, AI can customize lessons and feedback to each individual student’s needs, preferences, as well as learning speeds. Such personalization can bring about increased student participation in the process and improvement in academic results, allowing learners to progress at their own pace and receive support that is specifically targeted toward their individual problems. Equally, AI-enabled systems might provide educators with a highly detailed insight into the progress of every student to enable better and timely intervention. This not only makes the learning process more valuable but also enables teachers to handle heterogeneous classrooms better where students might have needs that are extremely varied.

2. Administrative Efficiency
Other than personalized learning, AI is also supposed to bring a huge difference in terms of administrative efficiency in schools. AI can automate mundane tasks such as scheduling, grading, and responding to communications from students. These are time-consuming tasks, and errors due to human mistakes pop up at critical moments. It is said that AI can revolutionize education, making it much more effective, efficient, and responsive to the needs of both the teacher and the student (Williamson, 2021). In other words, AI cuts down the administrative burden on educators so that they may spend more time teaching or providing any form of support to the students. In addition, AI-driven analytics also assist schools and universities in managing resources better. From classroom allocations to planning budgets, they make things vastly more efficient. This has a positive impact not only on educators but also, in turn, on the operational effectiveness of educational institutions, which may translate into good educational outcomes.

Ethical and Social Issues
1. Data Privacy and Surveillance
AI integration in education raises the issue of data privacy and surveillance. Most AI systems, after all, are powerful data-driven machines containing sensitive information regarding students’ learning habits, performance, and sometimes even their personal characteristics unless managed and protected. Such data could be used for something other than education, thus qualifying it as a violation of privacy and increased surveillance (Eubanks, 2018). It is clear that data privacy and security protection is crucial for students, since failure to do so will threaten to undermine confidence in educational institutions and deny students the full use of AI-based learning tools.

2. Bias and Inequality
The thing is, AI systems are not immune to bias, which gets embedded into training data; these biases often give rise to discriminatory outcomes in various educational uses of these AI applications. For example, an AI system trained on data that reflect historical inequities in education may continue to compound such inequities. Such is the case when data quality is unevenly distributed, resulting in AI systems that systematically favor the already well-resourced students. Application of this knowledge requires proactive efforts towards making AI systems representative, equitable in both design and deployment.

3. Addressing Economic and Inequality of Access
The looming threat of AI to deepen education-related inequalities is one of the pressing issues as we near 2030. Richer schools would benefit easily by incorporating new releases into the offerings to make them more competitive, whereas poorer schools fall behind without the wherewithal to do so. This will only further worsen the gap in educational quality and outcomes between the differing socio-economic groups. To avoid such a scenario, what is critical is for there to be policy provisions that ensure uniform availability of all these technologies in AI across all educational settings. Such policies could entail subsidizing AI technology in the under resourced schools, training the educators who have to operate within such surroundings, and designing AI-driven educational tools to be accessible and useful across differences in educational contexts.

Conclusion
In all probability, by 2030, the landscape of education would have been deeply influenced by the incorporation of AI technologies. While the promises of AI to improve personalization and efficiency in education are alluring, particular ethical challenges and equity concerns attend them. In this respect, strong policies and practices need to be put in place to help address such challenges. By setting guidelines for the ethical use of AI proactively and pursuing equitable access to technology, stakeholders can make certain that AI acts as a force to enhance educational outcomes across all sectors of society. The decisions made today will shape the educational realities of tomorrow, thereby demanding a deliberative approach to the introduction of AI in learning environments.

References

• Bozkurt, A., et al. (2023). Speculative Futures on ChatGPT and Generative AI: a Collective Reflection from the Educational Landscape. Asian Journal of Distance Education, 18(1).
• Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin’s Press.
• Macgilchrist, F., Allert, H., & Bruch, A. (2020). Students and society in the 2020s. Three future ‘histories’ of education and technology. Learning, Media and Technology, 45:1, 76-89, DOI: 10.1080/17439884.2019.1656235.
• Noble, S. U. (2018). Algorithms of Oppression: How Search Engines Reinforce Racism. NYU Press.
• Toyama, K. (2015). Geek Heresy: Rescuing Social Change from the Cult of Technology. PublicAffairs.
• Veletsianos, G., Houlden, S., Ross, J., & Sakinah, A., & Dickson-Deane, C. (2024). Higher education futures at the intersection of justice, hope, and educational technology. International Journal of Educational Technology in Higher Education. 21. 10.1186/s41239-024-00475-0.
• Williamson, B. (2021). The future of learning analytics and educational data sciences. Oxford Review of Education, 47(1), 1-18.

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