Data Privacy and Security for Adult Learners in AI Systems

 

By Lilian H. Hill

 

Artificial intelligence (AI) systems are now embedded in many adult learning environments, including learning management systems, adaptive learning platforms, writing and tutoring tools, learning analytics dashboards, and virtual advising systems. These technologies promise personalization, efficiency, and expanded access to learning. At the same time, they raise critical concerns about data privacy and security, especially for adult learners navigating education alongside their professional, familial, and civic responsibilities.

 

Understanding how AI systems collect, analyze, store, and protect learner data is essential for fostering trust, supporting ethical practice, and empowering adult learners to make informed decisions about their participation in AI-enabled learning environments.

 

Why Data Privacy Is Especially Important for Adult Learners

Data privacy for adult learners in AI systems hinges on data minimization, strong security (encryption, access controls), and transparency, ensuring only necessary data is collected and used ethically, with learners retaining control, while security measures like multi-factor authorization, encryption, and regular audits protect sensitive information from breaches, acknowledging that user inputs in GenAI can train the models, requiring caution about sharing private data. 

 

Adult learners differ from traditional-age students in ways that heighten the stakes of data privacy. Many adult learners are employed professionals whose learning data may intersect with workplace evaluations, licensure requirements, or career advancement. Others may be returning to education after long absences or engaging in learning to reskill in rapidly changing labor markets. These contexts make confidentiality, consent, and control over personal information particularly important (Kasworm, 2010; Rose et al., 2023).

 

AI systems collect extensive data, including demographic information, learning behaviors, written assignments, discussion posts, performance metrics, and engagement patterns. When these data are inadequately protected or used beyond their original purpose, adult learners may face risks such as loss of privacy, data misuse, reputational harm, or unintended surveillance (Azevedo et al., 2025; Prinsloo & Slade, 2017).

 

How AI Systems Use Learner Data

AI-driven learning technologies rely on data to function. Algorithms analyze learner inputs to personalize content, generate feedback, predict performance, or automate decision-making processes. While these capabilities can support learning, they also introduce complexity and opacity. Learners may not know what data are collected, how long they are retained, or how algorithmic decisions are made (Zuboff, 2019).

 

From an ethical perspective, transparency is critical. Responsible AI systems should clearly communicate what data are collected and why, how data are processed and analyzed, whether data are shared with third parties, how long data are retained, and what rights learners must access, correct, or delete their data. Without transparency, learners are asked to trust systems they may not fully understand, undermining autonomy and informed consent (Floridi et al., 2018).

 

Data Security Risks in AI-Enabled Learning

Beyond privacy, data security refers to the technical and organizational safeguards that protect information from unauthorized access, breaches, or misuse. Educational institutions and technology vendors increasingly store learner data in cloud-based systems, which can be vulnerable to cyberattacks if not adequately secured (Azevedo et al., 2015; Means et al., 2020).

 

Despite the rapid adoption of AI tools, institutional guidance on their responsible integration into higher education remains uneven. Where policies exist, they differ substantially in scope, enforceability, and levels of faculty involvement, leaving many educators uncertain about what is permitted, encouraged, or restricted (Azevedo et al., 2024). As a result, institutions face an increasing imperative to develop AI policies that not only address emerging risks but also provide faculty with clarity, support, and flexibility.

 

For adult learners, data breaches may expose not only academic information but also sensitive personal and professional details. Strong data security practices such as encryption, access controls, regular audits, and incident response planning are essential to minimizing these risks. Institutions have an ethical responsibility to ensure that efficiency and innovation do not come at the expense of learner protection.

 

Power, Surveillance, and Learning Analytics

AI systems in education often operate through learning analytics, which track and analyze learner behavior to inform instructional decisions. While analytics can identify students who need support, they can also create surveillance environments that disproportionately affect adult learners who balance learning with work, caregiving, or health challenges (Prinsloo & Slade, 2017).

 

When predictive models label learners as “at risk,” those classifications may shape how instructors, advisors, or systems interact with them. Without careful governance, such systems risk reinforcing bias, reducing learner agency, and privileging efficiency over human judgment (Selwyn, 2019).

 

Empowering Adult Learners Through Digital Literacy

Supporting data privacy and security is not solely a technical challenge; it is also an educational one. Adult learners benefit from opportunities to develop digital and data literacy, including understanding privacy policies, consent mechanisms, and the implications of sharing data with AI systems (Selwyn, 2016).

 

Educators and institutions can empower learners by explaining how AI tools work in an accessible language, providing choices about tool use when possible, modeling ethical and transparent data practices, and encouraging critical reflection on technology’s role in learning. Such practices align with adult learning principles that emphasize autonomy, relevance, and respect for learners’ lived experiences (Knowles et al., 2015).

 

Toward Ethical and Trustworthy AI in Adult Learning

As AI becomes more prevalent in adult education, data privacy and security must be treated as foundational—not optional—components of effective learning design. Ethical AI systems prioritize learner rights, minimize data collection to what is necessary, protect data rigorously, and involve learners as informed participants rather than passive data sources (Floridi et al., 2018).

 

For adult learners, trust is central. When learners trust that their data are being handled responsibly, they are more likely to engage meaningfully with AI tools, experiment with new forms of learning, and fully benefit from technological innovation. Protecting data privacy and security is therefore not only a legal or technical obligation, but a pedagogical and ethical one.

 

References

Azevedo, L., Robles, P, Best, E. &and Mallinson, D. J. (2025). Institutional policies on artificial intelligence in higher education: Frameworks and best practices for faculty. New Directions for Adult and Continuing Education 2025, 188, 70–78. https://doi.org/10.1002/ace.70013

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5

Kasworm, C. E. (2010). Adult learners in a research university: Negotiating undergraduate student identity. The Journal of Continuing Higher Education, 58(2), 143–151. https://doi.org/10.1177/0741713609336110

Knowles, M. S., Holton, E. F., & Swanson, R. A. (2015). The adult learner (8th ed.). Routledge.

Means, B., Bakia, M., & Murphy, R. (2020). Learning online: What research tells us about whether, when and how. Routledge.

Prinsloo, P., & Slade, S. (2017). An elephant in the learning analytics room: The obligation to act. Proceedings of the Seventh International Learning Analytics & Knowledge Conference, 46–55. https://doi.org/10.1145/3027385.3027406

Rose, A. D., Ross-Gordon, J. & Kasworm, C. E. (2023). Creating a place for adult learners in higher education: Challenges and opportunities. Routledge.

Selwyn, N. (2019). Education and technology: Key issues and debates (3rd ed.). Bloomsbury.

Selwyn, N. (2019). What’s the problem with learning analytics? Journal of Learning Analytics, 6(3), 11–19. https://doi.org/10.18608/jla.2019.63.3

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.

 

AI Skills Every Adult Learner Should Build

 

By Simone C. O. Conceição

 

As artificial intelligence (AI) continues to shape industries, education, and everyday life, adult learners must develop not only digital literacy but also AI literacy—the ability to understand, interact with, and make informed decisions about AI systems. These skills are increasingly essential in the workplace, in civic life, and for lifelong learning.

 

This blog post outlines the foundational AI-related competencies every adult learner should build and explains how educators and workforce programs can support them.

 

Why AI Skills Matter for Adult Learners

The rise of generative AI, intelligent assistants, and predictive analytics is transforming how people access information, perform tasks, and communicate. According to the World Economic Forum (2023), AI and big data are among the top emerging technologies, with 75% of companies expected to adopt AI in the next five years. Workers who understand and can use these tools effectively will be better positioned for jobs of the future.

 

AI literacy isn’t just about using ChatGPT—it includes understanding how AI works, recognizing its limitations, and applying it ethically. AI literacy requires a blend of conceptual, practical, and critical thinking skills.

 

Core AI Skills for Adult Learners

1. Understanding AI Concepts. Adult learners should grasp basic AI concepts, such as:

• What AI is (and isn’t)
• The differences between machine learning, generative AI, and automation
• How algorithms make decisions based on data

This foundational knowledge enables learners to evaluate the credibility, purpose, and potential impacts of AI systems they encounter.

 

2. Using AI Tools for Everyday Tasks. Learners should gain hands-on experience with common AI tools:

• Text generation (e.g., ChatGPT, Grammarly)
• Image generation (e.g., DALL·E)
• Voice-to-text or language translation apps (e.g., Otter.ai, Google Translate)
• Search and productivity tools powered by AI (e.g., Copilot, Google Assistant)

 

These tools can support learning, communication, accessibility, and workplace productivity.

 

3. Interpreting and Analyzing AI Outputs. It’s essential to evaluate the quality and limitations of AI-generated content:

• Does the AI response make sense?
• Is it factually accurate?
• What biases might be embedded?

 

This skill helps learners become informed consumers and avoid misinformation or overreliance on automation.

 

4. Understanding Data and Privacy. Since AI relies on data, learners should know:

• What types of data are collected and used
• The risks of sharing personal data with AI systems
• How to adjust privacy settings or choose ethical tools

 

Data literacy and informed consent are central to learner autonomy and digital rights.

 

5. Ethical Awareness and Responsible Use. Adult learners should reflect on:

• When and how to use AI in ways that align with ethical, academic, or workplace standards
• Issues of bias, discrimination, and accessibility
• The human impact of AI on jobs, privacy, and equity

 

Responsible use of AI is a key component of digital citizenship in the AI era.

 

How Educators Can Support AI Skill Development

To prepare adult learners for an AI-driven world, educators and programs can:

• Integrate AI tools into course assignments and digital skills training
• Host workshops on evaluating AI content or protecting digital privacy
• Foster discussion on AI ethics, workforce impact, and critical thinking
• Provide access to multilingual and inclusive AI tools
• Co-create policies with learners on acceptable AI use

 

These strategies support not just skill acquisition but learner empowerment.

---

Conclusion: Building AI Literacy for Lifelong Learning

AI is transforming the way adults live, work, and learn. By equipping adult learners with essential AI skills—understanding, using, analyzing, and questioning AI tools—educators can help them thrive in a rapidly changing world.

 

The AI Literacy Forum, part of the Adult Learning Exchange Virtual Community, offers a space to continue these conversations. Moderated by Drs. Simone Conceição and Lilian Hill, the forum supports adult educators, learners, and program designers in navigating the ethical, practical, and pedagogical dimensions of AI.

---

 

References

World Economic Forum. (2023). The Future of Jobs Report 2023. https://www.weforum.org/publications/the-future-of-jobs-report-2023/

 

Promoting Digital Equity in an AI Enhanced World

 

By Lilian H. Hill

 

In an era when artificial intelligence (AI) is advancing at an unprecedented rate, ensuring digital equity—fair access to technology, infrastructure, and literacy—is not just desirable but essential. According to the World Economic Forum, approximately 2.6 billion people lack internet access, placing large segments of the global population on the sidelines of the “Intelligent Age” (World Economic Forum, 2025). Without intentional efforts to include underserved communities, AI risks widening rather than narrowing social and economic inequalities.

 

Promoting digital equity in an AI-driven world involves ensuring equal access to devices and reliable internet, investing in digital and AI literacy programs designed for diverse communities, and establishing governance frameworks that mitigate bias and embed accountability in AI systems. Key strategies include funding for affordable broadband and hardware, developing tailored educational initiatives, and involving marginalized communities in the design and oversight of AI solutions.

 

Why Digital Equity Matters

AI technologies including adaptive learning platforms, translation bots, and data-driven healthcare tools offer tremendous potential to foster inclusion. Properly deployed, they can democratize access to education, healthcare, and economic opportunities. As noted by Dubey (2025), “AI can be a powerful stimulus for digital inclusion when deployed thoughtfully” (para. 3). However, these benefits are contingent upon foundational conditions: reliable connectivity, access to devices, and strong digital literacy. As the World Economic Forum has warned, many data-driven systems were not designed with equity in mind, raising the risk of reinforcing existing disparities (World Economic Forum, 2024).

 

 

Key Barriers to Equity in the AI Era

Limited infrastructure and connectivity continue to create barriers to participation in AI-driven economies, as many regions still lack reliable broadband access or adequate computing hardware (World Economic Forum, 2021). Even when access is available, digital literacy gaps persist. Simply owning a device does not ensure that individuals have the skills needed to use AI tools effectively, and research shows that socially disadvantaged students often encounter substantial digital skill and resource gaps when engaging with AI-based programming education (Park & Kim, 2021). Additionally, inequities can be reinforced when AI systems are developed without inclusive data or design practices, prompting scholars and global organizations to call for data-equity frameworks that emphasize inclusive design, responsible stewardship, and stronger accountability structures in AI development (Stonier et al., 2024).

 

Lacking AI literacy carries significant consequences for both workers and businesses in an economy where artificial intelligence increasingly shapes productivity, decision-making, and innovation. For individuals, limited AI literacy can lead to reduced employability, as many roles now require at least a basic understanding of how AI-driven tools operate—from automated scheduling systems to data-supported customer service platforms. Workers who cannot effectively use or interpret AI systems may struggle to compete for high-skill positions, face slower career advancement, or become vulnerable to job displacement as routine tasks become automated. In business settings, low AI literacy among employees can hinder adoption of new technologies, reduce operational efficiency, and create costly errors when AI outputs are misunderstood or misapplied. Organizations without an AI-literate workforce may fall behind competitors who leverage automation, analytics, and intelligent systems to streamline processes and innovate. Ultimately, insufficient AI literacy exacerbates inequality by concentrating opportunity among those with access to training and leaving others increasingly marginalized in a rapidly evolving digital economy. Countries can be left behind in AI when they lack the infrastructure, trained talent, data resources, policy support, or economic capacity needed to participate in AI development and adoption.

 

Strategies for Promoting Digital Equity

To ensure that AI supports rather than undermines equity, we can pursue five strategic actions: universal access, design for equity, inclusive AI literacy, policy support, and measurement and monitoring of outcomes. These strategies support inclusive innovation, continuous improvement, and sustainability. See Figure 1.

 

Figure 1: Strategies for AI Digital Equity

 

1.    Inclusive Innovation
Inclusive innovation centers on designing and deploying AI technologies in ways that expand access, reduce barriers, and ensure that historically marginalized communities benefit from digital transformation. This approach emphasizes building systems and infrastructure that are equitable from the outset, rather than retrofitting fairness after inequities have already emerged.

• Invest in universal access: Prioritize infrastructure investments such as broadband, devices, and power so that underserved communities can engage fully in the digital economy. Closing the digital divide is “urgent” if AI’s benefits are to reach all (World Economic Forum, 2025).
• Design for equity from day one: Embed principles of inclusivity, accessibility, and fairness in AI system design, including language support, cultural contexts, and equitable datasets. The IDEAS (Inclusion, Diversity, Equity, Accessibility, and Safety) framework offers a timely model for integrating these principles throughout the AI lifecycle (Zallio, Ike, & Chivăran, 2025).

2.    Continuous Improvement
Continuous improvement emphasizes the need for ongoing learning, adaptation, and collaboration to ensure AI systems remain equitable and responsive to community needs. This includes cultivating AI literacy, updating policies as technology evolves, and fostering partnerships that strengthen accountability and innovation.

• Advance inclusive AI literacy: Foster educational programs that help learners interact with, create with, and apply AI, especially in communities that historically lacked access (Digital Promise, n.d.).
• Support policies and partnerships: Government, industry, and civil society must collaborate to develop public–private partnerships, provide subsidies or incentives for equitable AI deployment, and enforce regulatory frameworks that protect marginalized populations (Stonier et al., 2024).

3.    Sustainability

 

Planning on sustainability focuses on building long-term, resilient systems that continually promote equity, transparency, and accountability. Sustainable AI ecosystems require consistent evaluation, responsible data governance, and mechanisms that ensure benefits endure across generations and technological shifts.

 

• Monitor and measure outcomes: Use frameworks such as the Global Future Council’s data equity model to assess progress and hold systems accountable for fair and inclusive outcomes (World Economic Forum, 2024).

 

A Future That Works for All

In a world increasingly shaped by AI, digital equity offers fairness and resilience. When all communities have access to the tools, knowledge, and power to engage with AI, we unlock richer innovation, more robust economies, and greater societal wellbeing. By contrast, if we allow gaps to expand, the risk is a bifurcated world where some flourish in an AI‑driven economy and others fall further behind.

 

In the end, promoting digital equity in the AI‑enhanced world means more than providing devices. It means rethinking systems, designing inclusively, and investing everywhere. If we keep people at the center, everyone has the chance to benefit, contribute, and lead

 

References

Digital Promise. (n.d.). AI and digital equity. https://digitalpromise.org/initiative/artificial-intelligence-in-education/ai-and-digital-equity/

Dubey, A. (2025). AI can boost digital inclusion and drive growth. World Economic Forum. https://www.weforum.org/stories/2025/06/digital-inclusion-ai/

Katona, J., Gyonyoru, K.I.K. AI-based Adaptive programming education for socially disadvantaged students: Bridging the digital divide. TechTrends, 69, 925–942 (2025). https://doi.org/10.1007/s11528-025-01088-8

Stonier, J., Woodman, L., Teeuwen, S., & Amezaga, K. Y. (2024). A framework for advancing data equity in a digital world. World Economic Forum. https://www.weforum.org/stories/2024/10/digital-technology-framework-advancing-data-equity/ 

World Economic Forum. (2021). Global technology governance report. World Economic Forum. https://www3.weforum.org/docs/WEF_Global_Technology_Governance_2020.pdf

World Economic Forum. (2024, September). Entering the intelligent age without a digital divide. https://www.weforum.org/stories/2024/09/intelligent-age-ai-edison-alliance-digital-divide/ World Economic Forum

World Economic Forum. (2025, January). Closing the digital divide as we enter the Intelligent Age. https://www.weforum.org/stories/2025/01/digital-divide-intelligent-age-how-everyone-can-benefit-ai/

Zallio, M., Ike, C. B., & Chivăran, C. (2025). Designing artificial intelligence: Exploring inclusion, diversity, equity, accessibility, and safety in human-centric emerging technologies. AI, 6(7), Article 143. https://doi.org/10.3390/ai6070143

 

 

The Role of AI in Inclusive Learning Environments

 

By Simone C. O. Conceição

 

As artificial intelligence (AI) becomes increasingly integrated into educational tools and systems, it holds the potential to advance inclusive teaching and learning—if applied with care and intentionality. AI can support learners with diverse needs, streamline accessibility features, and personalize learning pathways. At the same time, it can reinforce inequities if not thoughtfully designed and implemented.

 

This post explores how AI can promote inclusion in adult education, the challenges to be aware of, and strategies educators can use to ensure AI supports equitable learning environments for all.

 

What Is Inclusive Education in the Age of AI?

Inclusive education aims to ensure that all learners—regardless of ability, language, background, or identity—can access and fully participate in meaningful learning experiences. With AI, this vision expands beyond physical accessibility to encompass digital inclusion, personalized support, and equity in learning outcomes.

 

AI tools can help realize this vision by offering assistive technologies, adapting content in real time, and identifying learner needs through data-driven insights (UNESCO, 2021). However, true inclusivity depends not just on access to tools, but on how they are developed, selected, and used by educators.

 

Opportunities: How AI Can Support Inclusion

1. Adaptive Learning for Diverse Needs. AI can adjust the pace, format, and complexity of content based on a learner’s interactions. This is particularly beneficial for adult learners with varying literacy levels, learning differences, or limited prior experience in digital environments (Holmes et al., 2022).

Example: Adaptive platforms like ALEKS or Knewton Alta personalize instruction by identifying learning gaps and adjusting content delivery accordingly.

 

2. Assistive Technologies. AI powers tools like real-time transcription (e.g., Otter.ai), text-to-speech (e.g., Microsoft Immersive Reader), and automated captioning—all of which improve access for learners with disabilities or English language learners.

These tools align with Universal Design for Learning (UDL) principles, which emphasize providing multiple means of engagement, representation, and expression (CAST, 2018).

 

3. Multilingual and Cultural Accessibility. AI-driven translation tools, such as Google Translate or DeepL, can break down language barriers and support culturally diverse learners. Additionally, AI chatbots and voice assistants can be trained in various dialects and languages to offer support beyond the dominant culture.

 

4. Equity Through Predictive Analytics. Learning analytics supported by AI can help identify learners who may be falling behind—based on patterns in engagement or assessment data—and enable early intervention (Ifenthaler & Yau, 2020). When used ethically, this can prevent learners from being overlooked due to implicit bias or lack of visibility in online environments.

 

Challenges and Ethical Considerations

Despite these opportunities, there are risks that must be addressed to ensure AI truly serves inclusion:

• Bias in Training Data: If AI systems are trained on datasets that lack diversity, they may reproduce stereotypes or exclude underrepresented groups.
• Privacy Concerns: Collecting sensitive learner data for personalization or analytics raises questions about consent, surveillance, and autonomy.
• Technology Access Gaps: AI-powered tools often assume stable internet, updated devices, and digital fluency—conditions not all adult learners have.

 

Without intentional design, AI tools can unintentionally amplify exclusion rather than mitigate it.

 

Strategies for Ethical and Inclusive AI Use

Educators, designers, and institutions can take the following steps to promote inclusive AI use:

1. Evaluate Tools for Bias and Accessibility
   Choose vendors and platforms that are transparent about their algorithms and committed to accessibility standards.
2. Involve Diverse Learners in Design and Testing
   Co-design AI-enhanced tools with input from learners of different ages, abilities, and cultural backgrounds.
3. Provide Digital Literacy Support
   Ensure learners have the skills and support to use AI-powered tools confidently and critically.
4. Ensure Human Oversight
   Use AI as a support—not a replacement—for relational teaching, dialogue, and community-building.
5. Establish Data Ethics Protocols
   Be clear with learners about what data is collected, how it’s used, and what choices they have in the process.

---

Conclusion: Inclusion Must Be Intentional

AI is not inherently inclusive—but it can be a powerful tool for inclusion when paired with ethical practice, thoughtful pedagogy, and an unwavering commitment to equity. Integrating AI into education requires thoughtful consideration to ensure it advances equitable learning and protects the rights and needs of all students.

 

The AI Literacy Forum, hosted by the Adult Learning Exchange Virtual Community, offers a space for adult educators to discuss, question, and share resources related to equitable AI integration, moderated by Drs. Simone Conceição and Lilian Hill, the forum welcomes your voice in shaping a more inclusive digital learning future.

 

---

 

References

CAST. (2018). Universal Design for Learning Guidelines version 2.2. http://udlguidelines.cast.org

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., & Santos, O. C. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(4), 575–617. https://doi.org/10.1007/s40593-021-00239-1

Ifenthaler, D., & Yau, J. Y.-K. (2020). Utilising learning analytics to support study success in higher education: A systematic review. Educational Technology Research and Development, 68, 1961–1990. https://doi.org/10.1007/s11423-020-09788-z

UNESCO. (2021). AI and education: Guidance for policy-makers. https://unesdoc.unesco.org/ark:/48223/pf0000377071

 

Addressing Bias in AI: What Adult Educators Should Consider

 

By Lilian H. Hill

 

Artificial intelligence (AI) is increasingly shaping how people learn, work, and access information. From adaptive learning platforms to automated feedback tools, adult educators are finding themselves navigating opportunities and challenges that come with these technologies. One of the most pressing concerns is bias in AI systems, a complex issue that raises questions of fairness, equity, and responsibility in teaching and learning.

 

Concerns about biased algorithms predate the current popularity of artificial intelligence (Jennings, 2023). As early as the mid-1980s, a British medical school faced legal repercussions for discrimination after using a computer system to evaluate applicants. Although the system’s decisions mirrored those of human reviewers, it consistently favored men and those with European-sounding names. Decades later, Amazon attempted to streamline hiring with a similar AI tool, only to find it was disadvantaging women —an outcome rooted in biased training data from a male-dominated tech workforce.

 

OpenAI, the creator of ChatGPT and the DALL-E image generator, has been at the center of debates over bias since ChatGPT launched publicly in November 2022 (Jennings, 2023). The company has actively worked to correct emerging issues, as users flagged examples ranging from political slants to racial stereotypes. In February 2023, OpenAI took a proactive step by publishing a clear explanation of ChatGPT’s behavior, providing valuable insight into how the model functions and how future improvements are being shaped.

 

Understanding Bias in AI

Bias in AI occurs when algorithms produce outcomes that are systematically unfair or unbalanced, often due to the data used to train these systems. When the data reflects historical inequities, stereotypes, or informational gaps, AI may unintentionally reproduce or amplify those patterns (Mehrabi et al., 2022). For instance, résumé screening tools trained on past hiring data may undervalue applications from women or people of color (Dastin, 2018). Similarly, language models can generate content that perpetuates cultural stereotypes (Bender et al., 2021), and facial recognition systems may be less accurate for specific demographic groups, particularly individuals with dark skin (Buolamwini & Gebru, 2018). Understanding that AI bias often mirrors societal biases enables adult educators to engage with AI tools more critically and thoughtfully.

There are three primary sources of biased data: 1) use of biased training data, 2) human influence on training AI systems, and 3) lack of a shared understanding of bias.

 

1.    Biased Training Data

AI models learn from vast datasets that reflect the world as it is, including its prejudices. Just as humans are shaped by their environments, AI is shaped by the data it consumes, much of which comes from a biased internet. For instance, Amazon’s hiring algorithm penalized women because it was trained on historical data that was male-dominated. When datasets disproportionately represent particular groups or viewpoints, the model’s outputs reflect that imbalance. In short, there’s no such thing as a perfectly unbiased dataset.

 

2.     Human Influence in Training

After initial training, AI outputs are refined through Reinforcement Learning with Human Feedback (RLHF), in which human reviewers judge and rank responses. While this helps shape AI into behaving more like a “responsible” human, it also introduces personal and cultural biases. If all reviewers share similar backgrounds, their preferences will influence how the model responds, making complete neutrality impossible.

 

3.    No Shared Definition of Bias

Even if we could remove all data that reflects human bias, we would still face one unsolvable problem: people disagree on what bias means. While most can agree that discrimination is harmful, opinions vary widely on how AI should navigate complex social, political, or moral issues. Over-filtering risks producing a model that is so neutral it becomes unhelpful, stripped of nuance and unable to take a stand on anything meaningful.

 

Why This Matters for Adult Education

Adult learners bring diverse backgrounds, identities, and experiences into the classroom. AI tools built on non-representative data can worsen existing inequalities in education unless developers improve their training methods and educators use the technology thoughtfully (Klein, 2024). When AI tools are introduced without awareness of bias, the risk is that inequities become amplified rather than reduced (Holmes et al., 2022). For instance:

 

• Learners from marginalized groups may encounter materials or assessments that do not accurately represent their knowledge or potential.
• Automated tutoring or feedback systems may respond differently depending on dialects, accents, or language use.
• Predictive analytics used to flag “at-risk” learners could disproportionately affect specific student populations (Slade & Prinsloo, 2013).

 

Educators play a pivotal role in mediating these risks, ensuring that AI supports equity rather than undermining it.

 

What Adult Educators Should Consider

1. Critical Evaluation of Tools
• Ask: How was this AI system trained? What kinds of data were used?
• Explore whether the developers have published documentation about bias testing (Mitchell et al., 2019).
2. Transparency with Learners
• Explain how AI is being used in the classroom and its potential limitations.
• Encourage learners to evaluate outputs critically rather than accepting them at face value.
3. Centering Equity and Inclusion
• Select tools that offer options for cultural and linguistic diversity.
• Advocate for systems that are designed with universal access in mind (Holmes et al., 2022).
4. Ongoing Reflection and Adaptation
• Keep a reflective journal or log of how AI tools perform with different groups of learners.
• Adjust teaching strategies when inequities appear.
5. Collaborative Dialogue
• Create opportunities for learners to share their experiences with AI.
• Engage in professional learning communities where educators discuss emerging issues and solutions.

 

Moving Forward

AI literacy is more crucial than ever. When talking about AI with your adult learners, ensure they understand that these models are not flawless, their responses shouldn't be accepted as the absolute truth, and that primary sources remain the most reliable. Until better regulations are in place for this technology, the best approach is to "trust but verify." AI technologies are not neutral—they mirror the values, assumptions, and imperfections of the societies that create them. For adult educators, the challenge is not to reject AI outright but to engage with it thoughtfully, critically, and ethically. By proactively recognizing and addressing bias, educators can help ensure that AI contributes to inclusive, empowering learning environments.

 

References

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research, 81, 1–15. http://proceedings.mlr.press/v81/buolamwini18a.html

Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/idUSKCN1MK08G

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., Santos, O. C., & Koedinger, K. R. (2022). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 32(4), 731–761. https://doi.org/10.1007/s40593-021-00239-0

Jennings, J. (2023, August 8). AI in education: The bias dilemma. EdTech Insights. https://www.esparklearning.com/blog/get-to-know-ai-the-bias-dilemma/#:~:text=Some%20things%20teachers%20can%20do%20to%20help,use%20primary%20sources%20as%20the%20best%20sources

Klein, A. (2024, June 24). AI's potential for bias puts onus on educators, Developers. Center for Education Technology. https://www.govtech.com/education/k-12/ais-potential-for-bias-puts-onus-on-educators-developers#:~:text=Schools%20should%20be%20wary%20if,'%22

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022). A survey on bias and fairness in machine learning. ACM Computing Surveys, 55(6), 1–35. https://doi.org/10.1145/3457607

Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency, 220–229. https://doi.org/10.1145/3287560.3287596

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529. https://doi.org/10.1177/0002764213479366