Project on Open and Evolving Metaliteracies (POEM)
Created at Carnegie Mellon University, POEM is a dynamic collection of teaching resources that addresses crucial digital literacy challenges across three interconnected domains: understanding AI and algorithmic systems, working with data critically and ethically, and navigating media and disinformation. This peer-reviewed collection brings together practical classroom materials created by experts and educators who understand the urgency of teaching these topics in our rapidly evolving information landscape.
Available in both English and Spanish, POEM's learning objects will include engaging classroom activities, thoughtfully designed assignments, and interactive exercises that help high school and college students develop critical thinking habits as they increase their digital fluency and the ethical awareness needed to advocate for more just and equitable digital futures.
Teaching and Learning Object Keywords and Submission Guidelines
Overview
The POEM editorial collective invites submissions of openly licensed teaching and learning objects (TLOs) designed to support critical engagement with AI literacy, data literacy, and media/disinformation literacy. The following keywords serve as organizing concepts for the collection and reflect the interdisciplinary and evolving nature of metaliteracy. Each keyword below includes a description to guide contributors in aligning their TLOs with the goals of the project. TLOs can be submitted in English or in Spanish and should be aimed at a high school or college audience.
Keywords
Attention
Explores how AI-generated outputs, data visualization, and media are designed to capture and hold attention, examining persuasive design, recommendation systems, and attention economies across digital platforms and their impact on learning, behavior, and public discourse.
Authorship
Examines evolving concepts of creation and ownership in environments where AI generates content, data is collaboratively or extractively produced, and media is remixed. Encourages inquiry into originality, attribution, and ethical reuse.
Authority
Develops skills for evaluating credibility across AI systems, data sources, and media outlets. Investigates how authority is constructed, challenged, and algorithmically mediated.
Bias
Addresses the emergence and consequences of bias in algorithmic systems, data structures, and media narratives. Engages learners in identifying bias and its impact, and in developing strategies for mitigation.
Cognitive Dissonance
Explores how AI outputs, conflicting datasets, and contradictory media stories can create psychological discomfort, and how learners can engage critically with information that challenges their expectations or beliefs.
Critical Use
Promotes thoughtful and equitable engagement with AI tools, data analysis, and media content. Encourages reflective practices and awareness of systemic impact.
Data Creation and Curation
Focuses on the collection, structuring, and maintenance of data. Emphasizes the social and ethical implications of curatorial decisions and their impact on downstream use in AI and media.
Justice
Examines how digital systems can perpetuate or challenge systemic inequities. Encourages critical analysis through lenses of power, harm, and inclusion.
Equity
Explores participation, representation, and accessibility in digital environments. Addresses disparities in digital literacy, technology access, and infrastructural support.
Evaluation
Supports the development of skills to assess the reliability, quality, and relevance of AI outputs, data sets, and media content. Encourages multiple forms of evidence and contextual reasoning.
Geography and Space
Highlights how digital tools reflect and affect spatial dynamics-location tracking, geotagging, and spatial bias. Encourages exploration of spatial justice and digital divides.
Global Impact
Investigates how data, AI, and media systems operate globally and affect transnational communities. Explores disparities in development, access, policy, and cultural framing.
Management and Stewardship
Covers responsible data and knowledge management practices, including preservation, versioning, documentation, and sustainability of digital infrastructures.
Manipulation
Examines how information and systems are intentionally distorted to mislead. Focuses on techniques in AI-generated misinformation, skewed data representation, and media spin.
News Literacy
Builds skills to identify credible news sources, distinguish reporting from commentary, and understand the role of algorithms and monetization in shaping the news landscape.
Openness
Promotes transparency, reuse, and accessibility in AI development, data sharing, and media production. Encourages critical thinking about the values and boundaries of open systems.
Ownership
Investigates who controls and benefits from data, AI systems, and media outputs. Explores economic and legal frameworks for intellectual property and communal knowledge.
Privacy and Security
Analyzes the mechanisms and implications of digital surveillance. Explores privacy, profiling, and the governance of personal data in AI and media contexts. Addresses risks, protections, and ethical implications of surveillance, tracking, and digital exposure.
Prompting
Introduces effective querying techniques for interacting with AI systems. Encourages critical reflection on how question framing affects outputs.
Quality
Teaches learners to evaluate the rigor, accuracy, and appropriateness of information, emphasizing clarity of standards and the impact of automation on traditional metrics.
Representation
Analyzes how people, communities, and issues are depicted-or omitted-in data, AI outputs, and media. Investigates symbolic, statistical, and algorithmic inclusion and exclusion.
Revenue
Explores the economic models behind digital systems. Investigates how monetization affects the design, visibility, and distribution of content in AI, data, and media ecosystems.
Sustainability
Focuses on the environmental and social costs of digital technologies. Encourages inquiry into energy use, infrastructure, and long-term viability of data and AI systems.
Transparency
Explores the need for explainable AI, open data documentation, and clear sourcing in media. Emphasizes accountability and interpretability.
Visual Literacy
Covers interpretation and critique of visual materials-charts, AI-generated imagery, manipulated photos, and data displays-emphasizing how visual information can inform or mislead.
Submission Guidelines
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Eligibility: Open to educators, librarians, researchers, and creators working with high school or college learners.
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Formats Accepted: Lesson plans, syllabi, video tutorials, interactive exercises, podcasts, games, quizzes, assessments, etc.
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Language: English or Spanish (submissions will be translated into the other language).
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Review: Submissions will be peer-reviewed for relevance, clarity, accessibility, and alignment with the POEM vision.
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Submission Deadline: September 17, 2025
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Trina McCowan
Director of Library Services
Flagler College
She/Her/Hers
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