Artifact Link: 

Aspectos Éticos de las Tecnologías de Aprendizaje

AECT Standard 1: Content Knowledge

Candidates demonstrate the knowledge necessary to create, use, assess, and manage theoretical and practical applications of educational technologies and processes.

• Indicator: Ethics

  • Evidence: The conceptual differentiation between Ethics and Morality and the definition of professional codes of conduct.

  • Justification: I synthesized the philosophical foundations of ethics and applied them to the professional field of Educational Technology, demonstrating a mastery of the professional ethics required by AECT.

• Indicator: Creating

  • Evidence: The construction of the digital environment (Google Site) and the classification of tools like LMS, Assessment apps, and Generative AI.

  • Justification: I created a structured instructional resource that uses a systems approach to guide users through the complex landscape of K-12 educational technology.

AECT Standard 2: Content Pedagogy (Primary Alignment)

Candidates develop as reflective practitioners able to demonstrate effective implementation of educational technologies and processes based on contemporary content and pedagogy.

• Indicator: Assessing/Evaluating

  • Evidence: The analysis of the Harris v. Hingham (2024) case study regarding AI and academic integrity.

  • Justification: I evaluated the effectiveness and ethical implications of technology integration by analyzing how disciplinary policies align (or fail to align) with modern pedagogical needs in the era of AI.

• Indicator: Ethics

  • Evidence: The Análisis Multicultural section (evaluating avatars, names, and cultural contexts).

  • Justification: I provided a framework for selecting media and technologies that respect and celebrate the diversity of a multicultural community.

AECT Standard 3: Learning Environments

Candidates facilitate learning by creating, using, evaluating, and managing effective learning environments.

• Indicator: Ethics

  • Evidence: The detailed sections on Marco Legal (Privacy/Consent) and Uso Justo (Fair Use).

  • Justification: I developed a guide for maintaining a safe technology infrastructure, emphasizing student data protection (Consent models) and intellectual property rights (Copyright/Fair Use).

• Indicator: Diversity of Learners

  • Evidence: The Equidad Digital segment and strategies for mitigating the digital divide.

  • Justification: I proposed specific strategies (loaner programs, flexible requirements) to foster an inclusive learning community that empowers students from diverse socioeconomic backgrounds.

AECT Standard 4: Professional Knowledge and Skills (Primary Alignment)

Candidates design, develop, implement, and evaluate technology-rich learning environments within a supportive community of practice.

• Indicator: Collaborative Practice

  • Evidence: Estrategias de Creación Colaborativa (Involving colleagues and students in technology selection).

  • Justification: I established a model for collaborating with peers and subject matter experts to analyze learner needs and ensure technology choices are culturally and pedagogically sound.

• Indicator: Reflection on Practice

  • Evidence: The Visión Transformadora and final conclusions regarding the educator's role as an ethical model.

  • Justification: I reflected on how our use of technology serves as a "hidden curriculum," impacting professional growth and the digital citizenship of the learners we serve.

AECT Standard 5: Research (Primary Alignment)

Candidates explore, evaluate, synthesize, and apply methods of inquiry to enhance learning and improve performance.

• Indicator: Theoretical Foundations

  • Evidence: The integration of data from Gallup (2019), EdWeek (2022), and Education Week (2024).

  • Justification: I utilized contemporary research and statistical data to provide a theoretical foundation for the necessity of ethical integration in K-12 technology.

• Indicator: Ethics

  • Evidence: The application of legal precedents and institutional guidelines (UPR) in the research process.

  • Justification: I conducted this inquiry using accepted professional and institutional guidelines, ensuring that the research presented on the site is accurate, cited, and ethically sound.

Reflection Question 1: In light of the Harris v. Hingham case study and the lack of clear district policies on AI, how can educators balance the need for strict academic integrity with the pedagogical goal of fostering student autonomy in a technology-driven world?

The Harris v. Hingham case highlights a critical vulnerability in modern schooling: when policy lags behind technology, the resulting ambiguity creates a culture of "policing" rather than "mentoring." To bridge the gap between strict integrity and student agency, we must rethink the nature of the student-teacher contract. True autonomy isn't just about giving students freedom; it’s about teaching them to manage it. Instead of an outright ban on AI, educators can implement a "Process-Over-Product" framework. This involves requiring students to submit "AI-Usage Disclosures" or logs of their prompts, which maintains integrity by making the student’s thinking visible and ensuring the AI remains a tool for augmentation rather than a replacement for thought.

Furthermore, the litigation in Harris likely could have been avoided if the boundaries of "unauthorized aid" were clearly defined in a digital age. Educators can foster autonomy by involving students in the creation of classroom AI ethics; when students help define what "original work" looks like in a world with generative tools, they become stakeholders in upholding those standards rather than subjects of a rigid system. Ultimately, we must move toward "AI-resilient" assessments. Strict integrity is best protected not by software detectors, which are often unreliable, but by assignments that require personal reflection, local context, and lived experience, elements that AI cannot authentically replicate.

Reflection Question 2: Using the concept of Lente Inclusiva, How can we adapt a digital unit on ecosystems incorporating local environments and traditional knowledge?

Adapting a digital unit on ecosystems through the Lente Inclusiva requires shifting the focus from a universal, standardized curriculum to one that is profoundly situated in the student's own reality. This lens demands that we view technology not just as a delivery system for information, but as a bridge between scientific data and ancestral or community wisdom.

To implement this, the unit must first move away from generic digital simulations of distant biomes and instead use Place-Based Digital Mapping. Using tools like GIS, students can create "living maps" of their own backyards or neighborhoods. Through the inclusive lens, these maps are not restricted to biological data; they are enriched by interviewing local elders or community leaders to document traditional names for species, historical land uses, and indigenous ecological indicators. This approach validates the student’s own environment as a primary site of scientific importance, rather than a secondary one.

Furthermore, the lens emphasizes Epistemological Pluralism, where traditional knowledge  and Western science are presented as complementary rather than hierarchical. In a digital unit, this looks like a comparative analysis module where students evaluate local climate data alongside oral histories of weather patterns. By using the digital platform to archive and share these findings, perhaps through a community-facing website or a digital field guide, the students transition from passive consumers of a globalized curriculum to active curators of their own cultural and ecological heritage. This transformation ensures the "digital" remains human-centered, accessible, and deeply respectful of the diverse ways of knowing that exist within a local ecosystem.

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Why this works for your Portfolio:

• Critical Thinking: It shows you aren't just summarizing laws (COPPA/FERPA), but actually wrestling with the dilemmas teachers face daily.

• Alignment: It touches on Standard 2 (Pedagogy) and Standard 4 (Professional Knowledge) by positioning you as a leader who thinks about policy and digital citizenship.

• Closing the Loop: It connects your research on ethics (Standard 5) to a real-world scenario, proving that your portfolio artifact has practical application.

Artifact Link: 

Investigación Prospectiva y Modelado Predictivo

AECT Standard 1: Content Knowledge

Candidates demonstrate the knowledge necessary to create, use, assess, and manage theoretical and practical applications of educational technologies and processes.

• Indicator: Using

  • Evidence: Discussion of AI tools such as ChatGPT, Copilot, Gemini, and MagicSchool.ai.

  • Justification: I demonstrate the ability to select and use emerging technological resources to enhance pedagogy and support student learning through the lens of future-ready educational planning.

• Indicator: Assessing/Evaluating

  • Evidence: The use of Predictive Modeling to detect academic risk and dropout patterns.

  • Justification: I demonstrate how technology can be used to assess the effectiveness of instructional materials and student progress through data-driven evaluation.

AECT Standard 2: Content Pedagogy

Candidates develop as reflective practitioners able to demonstrate effective implementation of educational technologies based on contemporary content and pedagogy.

• Indicator: Creating

  • Evidence: Developing Flexible Curricula and personalized learning environments using AI.

  • Justification: I applied content pedagogy to create applications of AI that improve learning outcomes, focusing on the "personalization of content and evaluations" based on student pace.

• Indicator: Managing

  • Evidence: The focus on Predictive Modeling to provide supportive learning communities.

  • Justification: I explored how to manage technological processes to provide flexible and diverse learning environments that anticipate student needs before gaps occur.

AECT Standard 3: Learning Environments

Candidates facilitate learning by creating, using, evaluating, and managing effective learning environments.

• Indicator: Creating

  • Evidence: Designing scenarios for 2040 based on research-based best practices.

  • Justification: I created instructional design frameworks based on learning principles that prepare educational systems for future technological and social changes.

• Indicator: Diversity of Learners

  • Evidence: Strategies for Mitigating the Digital Divide and promoting educational equity.

  • Justification: This project focuses on fostering a learning community that empowers learners with diverse backgrounds by anticipating and closing potential gaps in technology access.

AECT Standard 4: Professional Knowledge and Skills

Candidates design, develop, implement, and evaluate technology-rich learning environments within a supportive community of practice.

• Indicator: Leadership

  • Evidence: Using Prospective Research to help institutions and governments take evidence-based decisions.

  • Justification: I demonstrate leadership by showing how educators can guide peers in designing and implementing technology-supported learning through long-term strategic visioning.

• Indicator: Reflection on Practice

  • Evidence: The comparison between Traditional vs. Prospective Literature Reviews.

  • Justification: I analyzed and interpreted the effectiveness of current research methods to enhance professional growth, moving from "predicting" to "constructing" a desired educational future.

AECT Standard 5: Research (Primary Alignment)

Candidates explore, evaluate, synthesize, and apply methods of inquiry to enhance learning and improve performance.

• Indicator: Theoretical Foundations

  • Evidence: The study of Futures Studies, Strategic Foresight, and Critical Discourse Analysis.

  • Justification: I demonstrated foundational knowledge of how research contributes to current theory in educational technology by analyzing how a community constructs visions of the future.

• Indicator: Method

  • Evidence: Application of Systematic Prospective Literature Review (2025–2028).

  • Justification: I applied rigorous research methodologies (filtering data, recognizing patterns with AI, and backcasting) to solve complex problems in technology-rich environments.

• Indicator: Ethics

  • Evidence: The section on Ethics and Equity in Predictive Modeling (avoiding algorithmic bias and protecting student privacy).

  • Justification: I demonstrated how to conduct research and apply AI practice using professional guidelines that prioritize student privacy and algorithmic transparency.

Reflection Question: How does the use of AI in a Systematic Literature Review change the role of the researcher from a 'data collector' to a 'critical synthesizer'?

The integration of Artificial Intelligence (AI) into the Systematic Literature Review (SLR) process fundamentally redefines the researcher’s role from a "data collector" to a "critical synthesizer." Traditionally, SLRs required researchers to dedicate hundreds of hours to the mechanical tasks of searching databases, screening thousands of abstracts, and manually extracting data points. These tasks, while essential for rigor, are essentially clerical and often leave little cognitive space for high-level analysis. AI tools now automate these procedural elements with incredible speed and accuracy, effectively handling the bulk of the "collection" phase.

This evolution elevates the researcher’s responsibility; they are no longer just finding information, but are instead tasked with the intellectual labor of designing complex search parameters, auditing AI outputs for hallucination or error, and interpreting the broader thematic landscape. The researcher becomes a curator of knowledge, focusing on identifying nuanced gaps in the literature and synthesizing disparate findings into a cohesive, forward-looking narrative that a machine cannot construct. The transition represents a move from quantitative management to qualitative mastery.

Artifact Link: 

Instrumentos de Puerto Rico

AECT Standard 1: Content Knowledge (Primary Alignment)

Candidates demonstrate the knowledge necessary to create, use, assess, and manage theoretical and practical applications of educational technologies.

• Indicator: Creating

  • Evidence: Development of a comprehensive Google Site featuring modules on instruments, genres, and composers.

  • Justification: I created an instructional learning environment using a variety of systems approaches, organizing content into digestible modules for early childhood learners.

• Indicator: Using

  • Evidence: Integration of YouTube videos, audio samples (e.g., "Lamento Borincano"), and interactive elements.

  • Justification: I selected and used technological resources to support student learning, allowing students to "see" and "hear" cultural history that might otherwise be abstract.

AECT Standard 2: Content Pedagogy

Candidates develop as reflective practitioners able to demonstrate effective implementation of educational technologies based on contemporary content and pedagogy.

• Indicator: Creating

  • Evidence: Using "MaraMaraca," "Güirito," and "Bombito" (Poe AI bots) to explain concepts.

  • Justification: I applied content pedagogy to create AI-driven applications that simplify complex musical concepts into age-appropriate, interactive dialogues.

• Indicator: Ethics

  • Evidence: Emphasis on the Afro-Boricua roots in the "Bomba" section and the multicultural identity of Puerto Rico.

  • Justification: I designed and selected media that emphasize the diversity of our society as a multicultural community, ensuring that the contributions of diverse ethnic groups are represented with respect.

AECT Standard 3: Learning Environments (Primary Alignment)

Candidates facilitate learning by creating, using, evaluating, and managing effective learning environments.

• Indicator: Creating

  • Evidence: Instructional design products like the story "El Tambor que contaba historias" and coloring activities.

  • Justification: I created instructional products based on research-based best practices for primary education (K-3), using storytelling and gamification to facilitate learning.

• Indicator: Diversity of Learners

  • Evidence: Multi-sensory activities (visual videos, auditory music clips, and kinesthetic "raspa" movements).

  • Justification: I fostered a learning community that empowers learners with diverse abilities by providing multiple means of representation and engagement.

AECT Standard 4: Professional Knowledge and Skills (Primary Alignment)

Candidates design, develop, implement, and evaluate technology-rich learning environments.

• Indicator: Collaborative Practice

  • Evidence: The creation of an ecosystem of specialized Chatbots (Pandi, Trin-Trin, etc.) that act as virtual "subject matter experts."

  • Justification: I designed a technology-supported learning environment that utilizes virtual subject matter experts to engage learners in a community of practice.

• Indicator: Assessing/Evaluating

  • Evidence: Interactive games and questions within the AI bots (e.g., "¡Bombito quiere jugar!").

  • Justification: I designed and implemented informal assessment plans that align with the learning goals of cultural appreciation and musical recognition.

AECT Standard 5: Research

Candidates explore, evaluate, synthesize, and apply methods of inquiry to enhance learning.

• Indicator: Theoretical Foundations

  • Evidence: Content synthesis of Puerto Rican musical history, from the origin of the Plena in Ponce to the legacy of Rafael Hernández.

  • Justification: I demonstrated foundational knowledge of cultural history and integrated it into a digital platform to solve the problem of making traditional history accessible to modern digital natives.

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💡 Portfolio Highlight:

When documenting this project, point out how you successfully used AI (Poe/Chatbots) not just as a tool for "writing," but as characters that provide personalized tutoring for children. This shows a very high level of innovation within the AECT standards.

Refection Question: How does the use of character-based AI (like Bombito or Trin-Trin) help lower the 'affective filter' for young students learning about complex historical and cultural topics?

The use of character-based AI, such as Bombito or Trin-Trin, plays a crucial role in lowering the "affective filter", the psychological barrier that can inhibit learning when students feel anxious or overwhelmed. For young students, complex historical and cultural topics can often feel abstract or intimidating when presented through traditional text or academic lectures. Character-based AI transforms this experience by personifying the subject matter, turning a one-way flow of information into a social, interactive experience.

When students interact with a friendly, culturally relevant digital character, the stress associated with "getting the right answer" is reduced, creating a safe environment for exploration and inquiry. This sense of companionship and playfulness encourages students to ask more questions and engage with difficult narratives, such as the complexities of cultural history,more deeply. By meeting children where they are through storytelling and relatable personas, these tools ensure that the emotional and cognitive load is balanced, allowing for a more profound and lasting connection to the material. The technology serves as a "social lubricant" for the brain, making rigorous content accessible through the lens of a trusted guide.

Reflection Question: When evaluating asynchronous distance learning programs like EVPR, how can instructional designers successfully balance the administrative need for automation and self-pacing with the pedagogical necessity of human teaching presence and formative feedback?

In evaluating asynchronous programs like the Escuela Virtual de Puerto Rico (EVPR), instructional designers must navigate the tension between administrative automation and the psychological needs of the learner. While high structure and modularity are essential for managing the diverse needs of nontraditional students, such as those balancing work, caregiving, or prior academic discontinuity, relying solely on these automated systems can increase "transactional distance". This distance is the communicative gap that occurs when high structural clarity is not met with sustained human dialogue. To achieve balance, designers should embed "teaching presence" directly into the platform's architecture, ensuring that the design itself acts as a surrogate for live instruction by providing predictable pathways and clear organizational signals that reduce learner ambiguity.

A critical strategy for maintaining this balance involves shifting the focus from simple content delivery to the intentional development of self-regulated learning skills. Because asynchronous environments place heavy demands on a student’s ability to manage time and persist independently, designers should integrate mandatory onboarding modules that focus on goal setting and planning before academic content is even accessed. This approach uses administrative automation to trigger a pedagogical intervention that prepares students for the autonomy the program requires. By providing personalized pacing guides and visual dashboards that track mastery, the system can automate the logistical aspects of schooling while giving students the cognitive tools they need to succeed without constant human supervision.

Finally, the balance between automation and pedagogy is most visible in the design of assessments. In many asynchronous models, automated practice and final exams risk becoming mere gatekeeping or sorting mechanisms that provide little educational value beyond a grade. To restore pedagogical necessity, designers must move toward a mastery-based model where automated feedback is immediate and actionable. This means programming assessments to explain incorrect responses and direct students to specific remediation resources, effectively transforming a machine-graded test into an instructional support tool. When these automated insights are paired with human "early-alert" systems, where data patterns of disengagement prompt direct outreach from staff, the technology serves to enhance, rather than replace, the vital human connection necessary for academic persistence.

Artifact Link:

 EDUC 8031 - Paradigms, Changes, and Technology

AECT Standard 1: Content Knowledge

Candidates demonstrate the knowledge necessary to create, use, assess, and manage theoretical and practical applications of educational technologies and processes.

• Indicator: Assessing/Evaluating

  • Evidence: The critical analysis of the term "Digital Natives" (Prensky, 2001) versus the evidence-based concept of "Selective Users" (Selwyn).

  • Justification: I demonstrate the ability to assess contemporary theories in the field and contrast them with current research to define what constitutes true digital competence in academic contexts.

• Indicator: Managing

  • Evidence: The proposal to integrate everyday technologies (TikTok, Instagram, WhatsApp) with specific academic purposes.

  • Justification: I identify how to manage non-traditional digital tools to transform passive entertainment consumption into active processes of synthesis, creativity, and academic communication.

AECT Standard 2: Content Pedagogy (Primary Alignment)

Candidates develop as reflective practitioners able to demonstrate effective implementation of educational technologies and processes based on contemporary content and pedagogy.

• Indicator: Creating

  • Evidence: The design of strategies for using short-form videos and micro-explanations to foster media literacy.

  • Justification: I apply pedagogical principles to create activities where technology is not just a delivery medium, but a tool for students to produce knowledge and develop critical thinking.

• Indicator: Ethics

  • Evidence: The section on explicitly teaching students to evaluate source credibility and the ethical use of digital platforms.

  • Justification: I design interventions that promote digital responsibility and respect for information integrity within an ecosystem often dominated by misinformation.

AECT Standard 4: Professional Knowledge and Skills (Primary Alignment)

Candidates design, develop, implement, and evaluate technology-rich learning environments within a supportive community of practice.

• Indicator: Leadership 

  • Evidence: The Literature Review regarding teacher attitudes toward AI and technology (citing Alieto et al., 2024; Langreo, 2026).

  • Justification: I demonstrate professional leadership by researching and synthesizing the attitudes and competencies of peers, providing a data-driven foundation for professional development and the adoption of innovations like AI.

• Indicator: Collaborative Practice

  • Evidence: The use of project management tools like Trello and Google Workspace for cooperative learning.

  • Justification: I design environments that foster peer collaboration, using digital management tools to structure and improve digital teamwork.

AECT Standard 5: Research

Candidates explore, evaluate, synthesize, and apply methods of inquiry to enhance learning and improve performance.

• Indicator: Theoretical Foundations

  • Evidence: The integration of multiple cross-disciplinary studies (Yue et al., 2024; Galindo-Domínguez et al., 2024) regarding teacher digital competence.

  • Justification: I synthesize academic research to provide a theoretical foundation for the relationship between teacher attitude, technological proficiency, and effective AI implementation.

Question: If a student's technological proficiency is 'selective' (high for entertainment, low for academics), how must the teacher's role shift from a 'tool provider' to an 'architect of digital academic literacy'?

When students exhibit "selective" technological proficiency, they often fall into the trap of the "digital native" myth, the assumption that being able to navigate social media or gaming platforms automatically translates to academic digital competence. To address this, the teacher’s role must fundamentally shift from being a mere "tool provider", someone who simply hands out links or devices, to an architect of digital academic literacy. This shift requires the educator to design intentional learning structures that bridge the gap between intuitive entertainment use and disciplined academic application. The architect does not assume the student knows how to learn digitally; instead, they build the cognitive scaffolding necessary for students to use technology for research, critical analysis, and professional communication.

As an architect, the teacher moves beyond the "what" of technology and focuses on the "how" and "why." This involves deconstructing complex academic tasks into manageable digital workflows, such as teaching students how to evaluate the credibility of a source rather than just how to use a search engine. The educator must "blueprint" the learning experience by integrating metacognitive strategies, helping students recognize when their entertainment-based habits (like skimming or multi-tasking) are hindering their academic performance. By creating an environment where digital tools are used with specific, scholarly intent, the teacher ensures that technology serves as a foundation for knowledge construction rather than just a distraction or a shortcut.

Finally, this architectural role involves fostering a sense of digital agency and responsibility. Instead of just providing tools, the teacher designs opportunities for students to curate their own digital academic identities and portfolios. This requires a transition from passive consumption to active, scholarly creation. By scaffolding these experiences, the teacher helps students translate their high technical intuition into a professional skill set, ensuring that their proficiency is no longer "selective" but comprehensive and transferable across diverse academic and professional landscapes. The goal is not just to use the tool, but to understand the infrastructure of digital information and how to inhabit it as a rigorous scholar.