AI-Assisted Feedback and Assessment: Opportunities and Limitations

 

By Lilian H. Hill

 

Knowledge assessment determines how well students have learned and evaluates the effectiveness of teaching content and strategies for future improvement (Hill, 2020). Research has shown that incorporating knowledge assessments and effective feedback during instruction can boost both student motivation and overall learning effectiveness (Minn, 2022). AI innovations in education promise faster, scalable, and personalized guidance for learning. While AI-based automation can reduce the labor-intensive aspects of conducting learning assessments, its true value lies in enabling a deeper understanding of students and freeing up time to respond creatively to teachable moments. A key priority with AI is ensuring that humans remain actively involved and in control, with attention given to all those participating in the process—students, educators, and others who support learners (U.S. Department of Education, 2023). This blog post explores the opportunities and limitations of using AI for feedback and assessment, along with best practices for effective integration.

 

Opportunities

AI-driven personalized inputs are revolutionizing education by creating dynamic, tailored learning experiences that foster student engagement, improve learning outcomes, and equip individuals with the skills needed to thrive in a rapidly evolving world. AI recognizes patterns within data and automates decisions to create an adaptive learning environment, a technology-enhanced educational system that uses data and algorithms to personalize instruction in real time, based on each learner’s performance, needs, and preferences. Effective adaptive learning environments depend on three key adaptations: (a) delivering precise, timely, and meaningful feedback during problem-solving, and (b) organizing learning content to match each student’s unique skill level and proficiency, and (c) enhancing formative assessment feedback loops.

 

1.    Timely and Scalable Feedback

AI feedback leverages advancements in natural language processing to provide automated, personalized evaluations that can be scaled according to predefined criteria. AI systems can deliver instant feedback at scale, which is valuable in large classes or for repetitive tasks. According to a 2025 review of educational measurement technology, AI-powered scoring and personalized feedback enhance consistency and speed in assessment delivery. Drawing on extensive linguistic databases, these systems generate responses that mimic human engagement with student work. This technology has sparked considerable discussion in academic contexts, with the potential to transform teaching and learning practices (Zapata et al., 2025).

 

2.    Personalized Input and Adaptive Growth

Adaptive learning systems are essential for delivering personalized experiences in online instruction, particularly for those courses with large enrollment, such as MOOCS or Intelligent Tutoring Systems. For example, in a randomized controlled trial involving 259 undergraduates, researchers found that students receiving AI-generated feedback showed significant improvements across various writing dimensions compared to traditional instruction, with particularly strong effects on organization and content development (Zhang, 2025). The intervention also revealed that students valued usefulness over surface ease of use.

 

3.    Enhanced Formative Assessment Loops

Technological interventions can create more personalized, timely feedback loops that facilitate deeper engagement with learning. Formative assessment has long been a central application of educational technology, as feedback loops are essential for enhancing teaching and learning. AI may enable richer feedback loops by supporting formative assessment—when paired intentionally with human oversight—helping teachers adapt instruction based on student progress.

 

Limitations and Key Concerns

Creating machine learning models that deliver meaningful, personalized, and authentic feedback demands substantial involvement from human domain experts. Choices about whose expertise is included, how it is gathered, and when it is significantly applied influence the relevance and quality of the feedback produced. These models also require ongoing maintenance and refinement to align with changing contexts, evolving theories, and diverse student needs. Without continuous updates, feedback can quickly become outdated or misaligned with current learner requirements. Key limitations include (a) concerns about AI system accuracy, (b) loss of contextual understanding and embedded bias, (c) overreliance that diminishes human interaction, and (d) important ethical and pedagogical challenges.

 

1.    Accuracy

Researchers have recorded numerous cases of AI systems making harmful decisions due to coding errors or biased training data. Such failures have rendered inaccurate teaching evaluations, caused job and license losses, and discriminated based on names, addresses, gender, and skin color. AI systems can sometimes exploit shortcuts without capturing the deeper intent of their designers or the domain’s full complexity. For instance, a 2017 image recognition system “cheated” by identifying a copyright tag linked to horse images instead of learning to recognize images of horses (Sample, 2017).

 

2.    Context Loss and Bias

Lindsay et al. (2025) note that the convenience of automation carries the risk of neglecting the distinct needs of minority or atypical learners because they are more difficult to standardize and address. For example, automated essay scoring (AES) systems often rely on surface features like essay length or keywords, making them insensitive to nuance, creativity, and accurate content understanding. In experiments with several chatbots, Taylor (2024) found that AI-generated feedback tends to be generic and provide variations of the same feedback for multiple students (Taylor, 2024). Algorithmic bias is also a concern. Models trained on unbalanced data can amplify cultural or linguistic disparities, potentially disadvantaging Black, Indigenous, and People of Color (BIPOC) or non‑native English speakers unless bias mitigation strategies are in place.

 

3.    Over-reliance and Reduced Human Interaction

Evidence suggests that when students depend too heavily on AI-generated feedback, their opportunities for critical reflection and dialogue diminish, both key foundations for higher-order thinking and deep learning. A recent comparative study found that students tend to mistrust AI feedback when it is not combined with human guidance, while academic staff were more open, especially if AI suggestions augmented rather than replaced instructor feedback (Henderson et al., 2025). Moreover, educators’ reflections indicate that adopting AI for meaningful feedback may serve to increase instructor workload and complexity compared to traditional teaching methods, especially when contextual interpretation is needed (Taylor, 2024).

 

4.    Ethical and Pedagogical Considerations

Generative AI tools raise essential ethical dimensions—notably involving participation, impact, fairness, and evolution over time. Unless systems are carefully designed to be inclusive, AI-generated feedback may marginalize minority learners with unique needs (Lindsay et al., 2025). The National Council on Educational Measurement’s AIME group has similarly stressed validity, equity, and transparency as pillars for responsible AI in educational measurement (Bulut et al., 2024). With thoughtful implementation, ethical frameworks, educator training, and human oversight, AI can enhance education without sacrificing critical thinking or integrity.

 

Best Practices for Implementation

• Keep humans in the loop. Use AI as a supplement, not a replacement, for instructor-led feedback and assessment.
• Pilot first. Collect user feedback on pilot deployments before full-scale adoption to ensure transparency, acceptance, and reliability.
• Disclose AI use. State clearly when AI tools produce summaries or initial feedback, including platform and prompt details when appropriate.
• Educate users. Teach students to interpret AI output critically and support educators in leveraging feedback meaningfully.
• Audit for bias and fairness. Apply algorithmic audits and explainable AI techniques to evaluate model performance across diverse groups.

 

References

Bulut, O., Beiting-Parrish, M., Casablanca, J. M., Slater, S. C., Jiao, H., Song, D. … Morilova, P. (2024). The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges. Journal of Educational Measurement and Evaluation, 5(3). https://doi.org/10.59863/miql7785

Henderson, M., Bearman, M., Chung, J., Fawns, T., Buckingham Shum, S., Matthews, K. E., & de Mello Heredia, J. (2025). Comparing Generative AI and teacher feedback: Student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education, 1–16. https://doi.org/10.1080/02602938.2025.2502582

Hill, L. H. (Ed.). (2020). Assessment, evaluation, and accountability in adult education. Stylus Publishing.

Minn, S. (2022). AI-assisted knowledge assessment techniques for adaptive learning environments. Computers and Education: Artificial Intelligence, 3, 100050. https://doi.org/10.1016/j.caeai.2022.100050

Sample, I. (5 November 2017). Computer says no: Why making Ais fair, accountable, and transparent is critical. The Guardian. https://www.theguardian.com/science/2017/nov/05/computer-says-no-why-making-ais-fair-accountable-and-transparent-is-crucial

Taylor, P. (2024, September 6). The imperfect tutor: Grading, feedback and AI. Inside Higher Education. https://www.insidehighered.com/opinion/career-advice/teaching/2024/09/06/challenges-using-ai-give-feedback-and-grade-students?utm_source=chatgpt.com

U.S. Department of Education, Office of Educational Technology (2023). Artificial intelligence and future of teaching and learning: Insights and recommendations. Washington, DC.

Zapata, G. C., Cope, B., Kalantzis, M., Tzirides, A. O. (Olnancy), Saini, A. K., Searsmith, D., … Abrantes da Silva, R. (2025). AI and peer reviews in higher education: Students’ multimodal views on benefits, differences and limitations. Technology, Pedagogy and Education, 1–19. https://doi.org/10.1080/1475939X.2025.2480807

Zhang, K. (2025). Enhancing Critical Writing Through AI Feedback: A Randomized Control Study. Behavioral Sciences, 15(5):600. https://doi.org/10.3390/bs15050600

 

Understanding Generative AI: Benefits, Risks, and Ethical Use

 

By Lilian H. Hill

 

Generative Artificial Intelligence (GenAI) refers to systems that can create new content such as text, images, music, or even video based on patterns learned from large datasets. Unlike traditional AI systems that classify or predict, generative models generate original content. These sophisticated tools are popular and widely available at a low cost. Tools like OpenAI’s ChatGPT, DALL-E, and Google’s Gemini are notable examples (Bommasani et al., 2021).

 

GenAI is rapidly transforming the way we work, create, and communicate. From producing human-like text and generating realistic images to assisting in software development and content creation, GenAI is no longer a futuristic concept; it’s a tool many of us are already using, knowingly or not. But as with any powerful technology, its potential comes with critical questions about benefits, risks, ethics, and responsible use.

 

Benefits of GenAI
GenAI offers a wide range of benefits across sectors by enhancing creativity, efficiency, and accessibility. Some key advantages include:

 

1.    Creativity and Content Generation. GenAI can produce text, images, music, code, and video, supporting creative professionals and everyday users. It enables rapid prototyping of ideas, assists in drafting content, and offers inspiration for writers, designers, educators, and artists.

 

2.    Efficiency and Automation. By automating repetitive or time-consuming tasks—such as summarizing documents, composing emails, or generating reports—GenAI saves time and increases productivity. In industries like marketing or journalism, it can streamline content creation workflows.

 

3.    Personalization. GenAI can tailor content to individual preferences or needs. For example, in education, it can create adaptive learning materials suited to different skill levels. In business, it can generate personalized marketing messages or customer support responses.

 

4.    Accessibility. Gen AI helps break down barriers to access by generating content in different formats and languages. For instance, it can convert text to audio, simplify complex language, or create visual aids, making information more inclusive for people with diverse needs.

 

5.    Support for Learning and Skill Development. Tools powered by GenAI can act as tutors or writing assistants, offering feedback, explanations, or examples. This empowers learners to practice and improve their skills in real-time, whether they’re learning a new language, writing an essay, or studying a complex concept.

 

6.    Innovation in Research and Development. GenAI accelerates discovery by simulating ideas, generating hypotheses, or assisting with data interpretation. In fields like drug discovery or materials science, it can suggest novel compounds or design prototypes more quickly than traditional methods.

 

Risks and Challenges

Despite its promise, GenAI presents several risks:

 

1.    Spreading Misinformation. AI-generated content can be used to create convincing fake news, propaganda, deepfakes, or misleading scientific papers, which can undermine trust and amplify social harm (Zellers et al., 2019). Fleming (2023) noted that AI tools can generate distorted historical accounts, enabling malicious actors to flood the public sphere with misinformation and hateful content. The global reach of social media enables falsehoods and conspiracy theories to spread instantly across borders.

 

2.    Bias and Fairness. Generative models can replicate and amplify the biases found in the data they were trained on, including stereotypes based on race, gender, or disability (Bender et al., 2021). This can lead to discriminatory output or harmful content, even when unintended. With the rise of GenAI, concerns around data justice have grown, as these technologies rely on large datasets that may carry embedded biases. For example, a GenAI-driven predictive policing system that draws from historically biased crime data could disproportionately target communities of color, leading to over-policing and further marginalization.

 

3.    Intellectual Property and Plagiarism. GenAI tools can produce text, images, music, and other forms of content that closely resemble or even replicate existing works that are often shared without clear attribution. This raises complex questions about authorship, originality, and ownership in both academic and creative domains (Crawford, 2021). Users may unknowingly commit plagiarism or violate intellectual property laws. The rapid proliferation of AI-generated content is prompting urgent discussions about how to define and protect original work in the age of GenAI.

 

4.    Environmental Impacts. Artificial intelligence is an extractive industry due to its significant environmental footprint. Training large AI models requires substantial computing power, resulting in high energy consumption. Data centers rely on extracting finite natural resources, such as lithium. This parallels traditional extractive industries by drawing heavily on both human and natural resources, often without equitable returns or sustainability safeguards (Crawford, 2021).

 

Ethical Use and Best Practices

Ethical use of GenAI begins with transparency. Users should disclose when AI-generated content is used, especially in educational, professional, or public communication contexts. For researchers and educators, citing tools appropriately and understanding their limitations is crucial.

 

Human oversight is essential. While AI can support decisions, it should not replace human judgment in contexts like grading, hiring, or healthcare. Ensuring accountability for AI-assisted decisions is crucial for maintaining trust and upholding ethical integrity (Floridi & Cowls, 2019). Inclusive and responsible design of AI systems requires incorporating diverse data, testing for bias, minimizing environmental impacts, and involving stakeholders, which is key to building technology that serves all members of society fairly.

 

Conclusion

GenAI is a powerful tool with immense potential to enhance human creativity and productivity. But to realize its benefits responsibly, we must remain vigilant about its risks and committed to ethical practices. As users, educators, researchers, and citizens, our role is to use GenAI wisely.

 

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

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., ... & Liang, P. (2021). On the opportunities and risks of foundation models. Stanford University. https://arxiv.org/abs/2108.07258

Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.

Fleming, M. (2023, June 13). Healing our troubled information ecosystem. Medium. https://melissa-fleming.medium.com/healing-our-troubled-information-ecosystem-cf2e9e8a4bed

Floridi, L., & Cowls, J. (2019). A unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019). Defending against neural fake news. Advances in Neural Information Processing Systems, 32, 9051–9062.

 

Using AI to Support Personalized Learning in Adult Education

 

By Simone C. O. Conceição

 

Artificial intelligence (AI) is rapidly transforming adult education by enabling more personalized, adaptive, and data-informed learning experiences. While traditional instruction often employs a one-size-fits-all approach, AI technologies can tailor content, pacing, and support to individual learner needs, making education more flexible, inclusive, and effective.

 

This blog post examines how AI is transforming personalized learning in adult education, the opportunities it presents, and the key considerations educators must address to ensure equity and effectiveness.

 

What Is Personalized Learning in the Age of AI?

Personalized learning refers to instructional approaches that adjust the learning experience to meet the diverse backgrounds, goals, and preferences of individual learners. AI enables this personalization by analyzing learner data—such as progress, performance, and behavior patterns—and using that data to adapt content, feedback, and learning paths.

 

According to Holmes et al. (2019), AI systems are capable of adapting based on learner interactions, offering tailored support that can boost both engagement and achievement. This is especially significant for adult learners, who often balance education with work and family responsibilities and need flexible, relevant, and time-efficient instruction.

 

Applications of AI in Personalized Adult Learning

1. Adaptive Learning Platforms
   AI-driven platforms, such as Smart Sparrow or Knewton, tailor content delivery in real-time, adjusting to each learner’s pace, knowledge gaps, and engagement levels.
2. Automated Feedback and Assessment
   Natural Language Processing (NLP) allows tools like Grammarly or Turnitin to provide immediate, formative feedback on writing, empowering learners to revise and improve without waiting for instructor input (Luckin et al., 2016).
3. Intelligent Tutoring Systems
   These systems simulate one-on-one instruction by providing scaffolding and hints, tracking learner responses, and adjusting difficulty (VanLehn, 2011). They are particularly effective in supporting adult learners in foundational subjects, such as math or language skills.
4. Recommendation Engines
   AI can recommend courses, videos, or resources aligned with a learner’s goals, past activities, and preferences, much like streaming platforms suggest media content.

 

Benefits for Adult Learners

AI-powered personalization supports adult learners by:

• Enhancing engagement through tailored content
• Increasing efficiency by focusing on areas of need
• Offering autonomy and flexibility in learning pace and format
• Supporting diverse learning goals—from career advancement to personal enrichment

 

Moreover, adult learners benefit from immediate feedback, self-paced progression, and 24/7 access to learning support—features that address common barriers such as time constraints, confidence gaps, or prior negative schooling experiences (Rose et al., 2015).

 

Challenges and Considerations

Despite its promise, AI-enhanced personalization is not without challenges:

• Data Privacy: Collecting detailed learner data raises concerns regarding consent, security, and the ethical use of such data.
• Algorithmic Bias: If AI systems are trained on biased data, they may reinforce existing inequities.
• Overreliance on Automation: AI should complement—not replace—human relationships and instructional judgment.
• Access and Equity: Not all learners have equal access to devices, connectivity, or digital literacy support.

 

To ensure equitable outcomes, educators and institutions must design with inclusion in mind, audit AI systems for bias, and maintain transparency with learners about how their data is used (Zawacki-Richter et al., 2019).

 

Recommendations for Educators and Program Designers

• Pilot and evaluate AI tools before full-scale implementation
• Use learner data ethically and responsibly
• Blend AI with human interaction to ensure instructors remain central to the learning process.
• Provide training for adult educators to understand and effectively utilize AI systems.
• Support digital literacy so all learners can benefit from AI-powered platforms.

 

Looking Ahead

As AI technologies continue to evolve, they offer enormous potential to enhance personalization in adult education. When implemented thoughtfully, AI can support learner-centered approaches that enhance outcomes, promote motivation, and alleviate barriers to access.

 

At the Adult Learning Exchange Virtual Community, we invite you to share your experiences, tools, and questions in the AI Literacy Forum, moderated by Drs. Simone Conceição and Lilian Hill. Together, we can explore how to harness AI for more inclusive, effective, and empowering adult learning.

 

References

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. https://discovery.ucl.ac.uk/id/eprint/1475756/

Rose, D. H., Harbour, W. S., Johnston, C. S., Daley, S. G., & Abarbanell, L. (2015). Universal Design for Learning in postsecondary education: Reflections on principles and their application. Journal of Postsecondary Education and Disability, 28(2), 135–151.

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-019-0171-0

 

How AI Is Shaping the Future of Work and Lifelong Learning

 

By Simone C. O. Conceição 

 

Artificial intelligence (AI) is no longer a futuristic concept—it is a present-day force driving change across industries, reshaping job roles, and redefining what it means to learn throughout life. For adult learners, educators, and workforce development professionals, understanding how AI is influencing work and lifelong learning is essential for staying current, competitive, and empowered.

This post examines how AI is transforming the workforce and learning systems, identifies key challenges, and discusses strategies for adult educators, trainers, and program designers to prepare learners for success in this evolving landscape.

 

The Impact of AI on the Workforce

AI is automating routine tasks, augmenting human decision-making, and generating new types of work across sectors. From healthcare and manufacturing to finance and education, AI technologies are streamlining operations and creating new efficiencies. At the same time, they are changing the skills required for employment. As a result, the types of jobs available—and the skills required to perform them—are undergoing rapid change.

 

The World Economic Forum (2023) estimates that by 2027, AI and automation will have displaced 85 million jobs globally, while also creating 97 million new roles that require different competencies, especially in analytical thinking, creativity, and digital literacy. Many of these new roles will require continuous skill upgrading, hallmarks of lifelong learning in the modern economy. 

 

These projections underscore the need for reskilling and ongoing professional development across all sectors, placing a premium on adaptability, digital fluency, and lifelong learning competencies that are not only desirable but also necessary. Jobs that involve predictable, repetitive tasks are most at risk of automation, while roles requiring human judgment, emotional intelligence, and adaptability are likely to expand in the future. As such, adult learners must not only upgrade their technical knowledge but also develop soft skills that machines cannot replicate.

 

Brynjolfsson and McAfee (2014) argue that while technology increases productivity and creates new opportunities, it also widens skill gaps and can exacerbate socioeconomic inequality if not accompanied by inclusive reskilling efforts. For this reason, integrating AI awareness into workforce development is essential—not just to prepare individuals for new roles, but to help them understand the larger forces shaping labor markets.

 

AI and Lifelong Learning

Lifelong learning, once a theoretical ideal, has become a practical necessity. AI is reshaping how learning happens in several ways:

• Personalized learning pathways: AI-powered platforms can tailor content to learners' needs, enabling them to progress at their own pace.
• Just-in-time training: AI systems can deliver microlearning modules or refresher content in real time based on job performance data.
• Predictive analytics: Institutions and employers use AI to identify learning gaps and tailor programs to evolving industry demands.
• Credentialing and upskilling: AI is facilitating the rise of short-term, skills-based credentials that align more closely with labor market trends.

For adult learners, especially those navigating career transitions or returning to education, these innovations offer flexible, relevant, and responsive options for growth.

 

Challenges and Considerations

Despite its potential, the integration of AI into work and learning presents serious challenges:

• Equity and access: Not all learners have equal access to technology or support systems, which can deepen existing educational and economic divides (Robinson et al., 2020).

• Algorithmic bias: AI systems trained on biased data may perpetuate inequalities in hiring, promotion, or learning recommendations, leading to unfair outcomes in hiring, admissions, and learning assessments (O’Neil, 2017).

• Digital literacy gaps: Many adult learners lack the foundational digital and data literacy skills necessary to engage with AI-enhanced systems.

 

Educators and policymakers must address these challenges to ensure that the benefits of AI are distributed in an equitable and ethical manner. These concerns underscore the need for intentional design of inclusive learning environments that support diverse learners and cultivate a critical awareness of how technology impacts educational and economic opportunities.

 

Preparing for an AI-Enhanced Future

To thrive in this new landscape, adult learners must cultivate AI literacy—the ability to understand, interact with, and evaluate AI technologies. Educators, trainers, and program designers play a key role in equipping adults with the mindset and skills to thrive in an AI-enhanced society. Effective strategies include:

• Integrating discussions of AI and automation into workforce readiness programs
• Promoting project-based and experiential learning that engages learners with real-world AI tools
• Encouraging critical reflection on the social and ethical dimensions of AI
• Creating accessible, flexible learning pathways that account for learners' varying levels of tech proficiency

 

AI is not a replacement for human talent—it is a tool that can expand opportunities when used thoughtfully and inclusively. As noted by Schleicher (2018) of the OECD, education systems must shift from preparing learners for specific jobs to equipping them with lifelong competencies, including learning how to learn, adapting to change, and making informed choices in complex environments.

 

Join the Conversation

The AI Literacy Forum at the Adult Learning Exchange Virtual Community provides a platform for educators, practitioners, and learners to explore how AI is transforming work and lifelong learning. Moderated by Dr. Simone Conceição and Dr. Lilian Hill, the forum fosters critical conversations, resource sharing, and professional collaboration.

 

We invite you to join the conversation and help shape a future where AI enhances—not replaces—human potential in work and learning.

 

References

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. W. W. Norton & Company.

O’Neil, C. (2017). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.

Robinson, L., Cotten, S. R., Ono, H., Quan-Haase, A., Mesch, G., Chen, W., ... & Stern, M. J. (2015). Digital inequalities and why they matter. Information, communication & society, 18(5), 569-582.

Schleicher, A. (2018). The future of education and skills: Education 2030. The future we want. OECD Education Directorate.

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

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AI Jargon Explained: Key Terms Adult Learners Should Know

Image credit: Google DeepMind on Pexels

 

By Lilian H. Hill

 

While it may seem like jargon to non-experts, Artificial Intelligence (AI) terminology is a specialized vocabulary that describes the concepts, technologies, and processes enabling machines to replicate aspects of human intelligence. As AI transforms industries such as healthcare, finance, and manufacturing, familiarity with this vocabulary is essential for staying current with ongoing AI developments and innovations. The terminology presented here contains commonly used vocabulary in AI. To aid in comprehension, the terms are categorized into foundational terms, key concepts, practical applications, and concerns associated with AI.

 

Foundational AI Terms

Algorithm: A set of rules or procedures used by an AI system to perform tasks, such as sorting data or identifying patterns. Algorithms are the step-by-step instructions that guide every AI model (Cormen et al., 2009).

 

Artificial Intelligence (AI): Refers to the development of computer systems capable of performing tasks typically requiring human intelligence, such as perception, reasoning, learning, and decision-making (Russell & Norvig, 2020).

Large Language Model (LLM): Advanced AI systems trained with vast amounts of text data to understand and generate human-like language.

Machine Learning (ML): A subset of AI that involves the use of algorithms and statistical models that enable computers to learn from data and improve their performance without being explicitly programmed (Murphy, 2012). ML is foundational to most current AI applications. Deep learning, supervised learning, and unsupervised learning are different types of machine learning:

·       Deep Learning: A branch of machine learning involving neural networks with multiple hidden layers, enabling the modeling of complex, high-level abstractions in data such as image or speech recognition (Brown et al., 2020; Goodfellow et al., 2016).

·       Supervised Learning: A machine learning method where a model is trained on labeled data to learn the mapping from inputs to outputs (Hastie et al., 2009).

·       Unsupervised Learning: A form of machine learning that identifies patterns or groupings in unlabeled data (Murphy, 2012).

 

Neural Network: A computational model inspired by the human brain’s network of neurons, designed to recognize patterns and make predictions. These models are the backbone of many AI systems today (LeCun et al., 2015).

Key Concepts in AI

Natural Language Processing (NLP): The study and application of techniques that allow machines to understand, interpret, and generate human language (Jurafsky & Martin, 2020). It underpins applications such as chatbots and language translation tools.

Generative AI: A type of AI that can produce original content such as text, images, or music by learning patterns from training data. Examples include text generation, image generation, and music generation (Bommasani et al., 2021):

·       Text generation. a type of large language model that uses deep learning and transformer architecture to understand and generate human-like text (Brown, 2000). Chat Generative Pre-trained Transformer, more commonly known as ChatGPT, is a popular example.

·       Image Generation: an AI model that generates images from textual descriptions using deep learning. It can create original, coherent, and contextually relevant images from complex natural language prompts. One example is DALL·E.

·       Music Generation: Music created, composed, or produced with the aid of artificial intelligence involves the use of AI algorithms and models trained on extensive datasets of existing music. These systems can generate new musical content—including melodies, harmonies, rhythms, and lyrics. Suno AI is an example.

Training Data: The labeled or unlabeled dataset used to teach a machine learning model how to identify patterns and make predictions. The quality and diversity of training data heavily influence the accuracy of the model (Zhou et al., 2019).

Practical AI Applications

Automation: The use of technology, including AI, to perform tasks with minimal human intervention. Automation can increase efficiency but also raises concerns about labor displacement (Brynjolfsson & McAfee, 2014).

 

Chatbot: An AI application designed to simulate conversation with human users, often using NLP to interpret queries and generate responses. They are widely used in customer service and education to respond to routine inquiries (Shum et al., 2018).

Computer Vision: A field of AI that enables computers to interpret and make decisions based on visual data, such as images or video. It is used in facial recognition, medical imaging, and autonomous vehicles (Szeliski, 2010).

Intelligent Learning Management Systems (ILMS): Integrate AI-powered interactive features into traditional LMS that automate content management, personalize learning, boost engagement, improve accessibility, enable real-time communication and assessment, and deliver curated content (Hill & Conceição, 2024).

 

Concerns With AI

Bias: Systematic errors in AI outputs resulting from biased data, flawed algorithms, or inequitable system design. Bias is a significant ethical concern in the development of responsible AI (Barocas et al., 2019).

 

Black Box: A term used to describe AI systems whose decision-making processes are not transparent or interpretable, making it difficult to understand how conclusions are drawn (Burrell, 2016).

Explainability (Interpretability): The degree to which a human can understand the internal logic or decision-making process of an AI model. High explainability is critical in domains like healthcare and criminal justice (Doshi-Velez & Kim, 2017).

 

Hallucination: A phenomenon where AI models produce outputs that are plausible, but factually incorrect or nonsensical. For example, a chatbot that provides a reference to a nonexistent source.

Singularity
A hypothetical future point when AI surpasses human intelligence, potentially leading to rapid, uncontrollable changes in society. Though speculative, it raises questions about AI safety and governance (Kurzweil, 2005).

These terms provide a foundational understanding for adult learners venturing into the realm of AI. 

 

Join Our Conversation

At the Adult Learning Exchange Virtual Community, we invite you to share your experiences, tools, and questions in the AI Literacy Forum, moderated by Drs. Lilian Hill and Simone Conceição. Together, we can explore how to harness AI for more inclusive, effective, and empowering adult learning.

  

References

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