Impacts of LLM AI on Accessibility, Accuracy, and Critical Thinking

1. Executive Summary

Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence, with significant implications for accessibility, both in terms of assisting people with disabilities and broadening access to informed studies. The current state of LLM technologies, their capabilities, and limitations, are examined, with a particular focus on their impact on accessibility. The differences between ‘raw’ LLM use and more refined tools like You.com’s ARI, which leverages existing publications for analysis are drawn out. Additionally, it is concluded that the fallibility of LLMs can potentially promote stronger learning and critical thinking skills

2. Introduction to LLM AI Technologies

Large Language Models represent a significant advancement in artificial intelligence, leveraging deep learning techniques to process and generate human-like text. These models, such as OpenAI’s GPT-4, Google’s Gemini, and Meta’s LLaMA, are trained on vast datasets and have become integral to various applications, including natural language processing, content generation, and decision-making.

2.1 Capabilities of Current LLM AI Technologies

  1. Natural Language Processing and Understanding: LLMs excel in natural language processing (NLP) tasks, including text generation, summarization, translation, and sentiment analysis. They are designed to understand and generate human-like text, making them versatile tools for a wide range of applications [1] [2].

  2. Multimodal Capabilities: Modern LLMs, such as GPT-4o and Gemini 2.0 Flash, have multimodal capabilities, allowing them to process and generate text, images, audio, and even video. This enables their use in diverse fields, including medical imaging, video generation, and accessibility tools [3] [4] [5].

  3. Reasoning and Problem-Solving: Advancements in reasoning capabilities have enabled LLMs to perform complex tasks, such as step-by-step problem-solving, logical reasoning, and decision-making. Models like Claude 3.7 Sonnet and DeepSeek R1 are specifically designed to handle reasoning tasks with high precision [6].

  4. Customization and Domain Specialization: LLMs can be fine-tuned for specific industries or tasks, such as healthcare, finance, and education. This customization enhances their relevance and effectiveness in specialized applications [7].

  5. Integration with External Tools: LLMs are increasingly integrated with external tools and APIs, enabling them to perform tasks such as web retrieval, code interpretation, and real-time data analysis. This integration expands their functionality and applicability [8].

  6. Generative AI Applications: LLMs are at the forefront of generative AI, capable of creating text, code, images, and videos. They are used in content creation, marketing, and even scientific research, where they assist in generating hypotheses and analyzing data [9].

  7. Accessibility Enhancements: LLMs contribute to accessibility by providing tools for people with disabilities, such as speech-to-text systems, language translation, and assistive technologies. These applications improve communication and access to information for individuals with diverse needs [10].

  8. Real-Time Interaction: Some LLMs, like GPT-4o, offer real-time interaction capabilities, making them suitable for applications requiring immediate responses, such as customer service and conversational agents [11].

2.2 Limitations of Current LLM AI Technologies

  1. Accuracy and Reliability: Despite their capabilities, LLMs often struggle with accuracy. Studies indicate that generative models are truthful only 25% of the time, and their accuracy drops significantly for complex or expert-level tasks [12] [13]. This limitation poses challenges in applications requiring high precision, such as medical diagnostics and legal analysis.

  2. Bias and Ethical Concerns: LLMs are trained on large datasets that may contain biases, leading to the propagation of stereotypes and unfair outcomes. Bias mitigation remains a critical area of focus in LLM development [14].

  3. Energy Consumption and Environmental Impact: Training and operating LLMs require substantial computational resources, resulting in high energy consumption. For instance, training GPT-3 consumed 1,287 MWh of energy, highlighting the environmental cost of these technologies [15].

  4. Data Privacy and Security: LLMs are vulnerable to data leakage and privacy breaches, as they are trained on vast datasets that may include sensitive information. Ensuring data security and compliance with regulations is a significant challenge [16] [17].

  5. Hallucinations and Misinformation: LLMs have a tendency to “hallucinate,” generating false or misleading information. This limitation undermines their reliability and can lead to the dissemination of misinformation [18] [19].

  6. Contextual Understanding: Maintaining contextual understanding over extended interactions remains a challenge for LLMs. They often lose track of the conversation’s context, leading to irrelevant or incoherent responses [20].

  7. Scalability and Cost: The scalability of LLMs is constrained by their high computational and financial costs. Smaller organizations may find it difficult to adopt these technologies due to resource limitations [21].

  8. Ethical and Societal Impacts: The widespread adoption of LLMs raises ethical concerns, including job displacement, intellectual property issues, and the potential misuse of AI for malicious purposes. Addressing these concerns requires robust governance and regulatory frameworks.

  9. Limited Explainability: LLMs operate as “black boxes,” making it difficult to understand their decision-making processes. This lack of transparency hinders trust and accountability in critical applications [22].

  10. Dependency and Critical Thinking: Overreliance on LLMs can diminish critical thinking skills, as users may accept AI-generated outputs without scrutiny. This dependency poses risks in educational and professional settings.

3. Impact of LLMs on Accessibility for People with Disabilities

Large Language Models have demonstrated significant potential in enhancing accessibility for people with disabilities, offering innovative solutions to address barriers in communication, education, and digital interaction. However, their implementation also presents challenges that need to be carefully considered and addressed.

3.1 Capabilities of LLMs in Enhancing Accessibility

3.1.1 Improving Assistive Technologies

LLMs have shown the ability to enhance various assistive technologies, making them more effective and user-friendly:

3.1.2 Personalization and Adaptability

LLMs offer the potential for highly personalized and adaptable solutions to meet the diverse needs of users with disabilities:

3.1.3 Broadening Access to Information

LLMs play a crucial role in making information more accessible to diverse audiences:

3.1.4 Automating Accessibility Testing

LLMs are proving to be valuable tools in enhancing automated accessibility testing:

3.2 Limitations and Challenges of LLMs in Accessibility

While LLMs offer significant benefits for accessibility, they also present several challenges that need to be addressed:

3.2.1 Bias and Discrimination

LLMs inherit biases from their training data, which can perpetuate stereotypes and marginalize individuals with disabilities:

3.2.2 Fallibility and Inaccuracy

The tendency of LLMs to generate inaccurate or misleading information poses significant risks in accessibility applications:

3.2.3 Ethical and Privacy Concerns

The use of LLMs in accessibility applications raises important ethical questions:

3.2.4 Limited Representation in Development

The underrepresentation of people with disabilities in the development of LLM technologies leads to a lack of critical perspectives:

3.3 Broader Implications of LLMs on Accessibility

The impact of LLMs on accessibility extends beyond individual applications, with broader implications for digital inclusivity and educational equity:

3.3.1 Promoting Digital Inclusivity

LLMs have the potential to bridge the accessibility gap by making digital content and services more inclusive:

3.3.2 Advancing Educational Equity

LLMs can transform education for students with disabilities by providing personalized learning experiences:

4. Broadening Access to Informed Studies

Large Language Models (LLMs) are playing a transformative role in democratizing access to knowledge and informed studies. Their capabilities in processing and generating human-like text have opened up new avenues for research, education, and information dissemination. This section explores how LLMs are broadening access to informed studies, focusing on their capabilities, limitations, and the implications for accessibility and critical thinking.

4.1 Capabilities of LLMs in Broadening Access to Informed Studies

4.1.1 Democratization of Knowledge

LLMs, such as GPT-4, GPT-J, and BLOOM, have significantly contributed to the democratization of knowledge by making advanced AI tools accessible to a broader audience:

4.1.2 Facilitating Research and Literature Reviews

LLMs are increasingly being used to assist researchers in conducting literature reviews and synthesizing information from vast datasets:

4.1.3 Enhancing Accessibility for Non-Specialists

LLMs have the potential to make complex scientific knowledge more accessible to non-specialists:

4.1.4 Multilingual Capabilities

The multilingual capabilities of LLMs further broaden access to informed studies by breaking down language barriers:

4.1.5 Support for Special Educational Needs (SEN)

LLMs are being integrated into educational systems to support students with special educational needs:

4.2 Limitations and Challenges

While LLMs offer significant potential in broadening access to informed studies, they also present several challenges that need to be addressed:

4.2.1 Fallibility and Risk of Inaccuracies

One of the primary limitations of LLMs is their susceptibility to generating inaccurate or misleading information:

4.2.2 Bias in Training Data

LLMs are trained on vast datasets that may contain inherent biases:

4.2.3 Digital Divide

The digital divide remains a significant barrier to the widespread adoption of LLMs:

4.2.4 Ethical and Privacy Concerns

The use of LLMs in informed studies raises ethical and privacy concerns, particularly regarding the handling of sensitive data:

4.3 Promoting Critical Thinking Through LLM Fallibility

While the fallibility of LLMs is often viewed as a limitation, it can also serve as a catalyst for promoting critical thinking:

4.4 Tools Like You.com’s ARI and Their Role in Informed Studies

Tools like You.com’s ARI represent a significant advancement in the application of LLMs for informed studies:

5. Impact of LLM Fallibility on Learning and Critical Thinking

The fallibility of Large Language Models (LLMs) has significant implications for learning and critical thinking. While their inaccuracies and inconsistencies pose challenges, they also present unique opportunities to enhance cognitive processes when managed responsibly. This section examines both the negative and positive impacts of LLM fallibility on learning and critical thinking.

5.1 Negative Impacts of LLM Fallibility on Learning and Critical Thinking

5.1.1 Erosion of Independent Thinking Skills

The fallibility of LLMs can lead to over-reliance on AI-generated outputs, which diminishes students’ ability to think critically and independently:

5.1.2 Propagation of Misinformation

LLMs’ tendency to hallucinate or generate biased outputs can mislead learners, especially those who lack the critical evaluation skills to discern accurate from inaccurate information:

5.1.3 Impediments to Critical Thinking Development

LLMs’ fallibility can hinder the development of critical thinking by providing overly simplified or incorrect solutions to complex problems:

5.2 Positive Impacts of LLM Fallibility on Learning and Critical Thinking

Despite these challenges, the fallibility of LLMs also presents opportunities to enhance critical thinking and learning when managed responsibly:

5.2.1 Encouraging Critical Evaluation

The inaccuracies and inconsistencies in LLM outputs can serve as a catalyst for critical evaluation:

5.2.2 Facilitating Collaborative Learning

LLMs can act as collaborative partners in the learning process, guiding students through problem-solving strategies and encouraging them to refine their reasoning:

5.2.3 Opportunities for Self-Correction

Recent research suggests that LLMs can learn from their mistakes through methods such as self-rethinking and iterative feedback loops:

5.3 Strategies to Mitigate Negative Impacts and Enhance Learning

To maximize the benefits of LLMs while minimizing their drawbacks, educators and developers must adopt targeted strategies:

  1. Promote Critical Thinking Skills: Integrate LLMs into curricula in ways that encourage students to critically evaluate AI-generated content rather than passively accepting it [94]. This approach can involve teaching students to identify potential biases, fact-check claims, and consider alternative perspectives.

  2. Enhance Feedback Literacy: Teach students how to interpret and utilize feedback from LLMs effectively, reducing biases and improving trust in AI systems [95]. This skill involves understanding the strengths and limitations of AI-generated feedback and using it as a tool for reflection and improvement rather than as an absolute authority.

  3. Implement Responsible Use Policies: Develop guidelines for the ethical and responsible use of LLMs in educational settings, emphasizing the importance of independent thinking [96]. These policies should address issues such as academic integrity, proper attribution, and the appropriate contexts for using AI assistance.

  4. Leverage Advanced Prompting Techniques: Use methods such as chain-of-thought prompting and retrieval-augmented generation to improve the accuracy and reliability of LLM outputs [97] [98]. These techniques can help students understand the reasoning process behind AI-generated responses and encourage them to apply similar structured thinking in their own problem-solving.

  5. Incorporate LLM Fallibility into Curriculum: Design learning activities that explicitly address the limitations and potential errors of LLMs, using these as opportunities to develop critical analysis skills. This approach can include exercises in identifying and correcting AI-generated errors, comparing multiple AI outputs, and evaluating the reliability of different information sources.

  6. Foster Collaborative AI Use: Encourage group projects and discussions that involve the critical evaluation of LLM outputs. This collaborative approach can help students learn from each other’s perspectives and develop a more nuanced understanding of AI capabilities and limitations.

  7. Develop AI Literacy Programs: Implement comprehensive AI literacy programs that teach students about the underlying principles of LLMs, including their training processes, potential biases, and ethical considerations. This knowledge can empower students to use AI tools more effectively and responsibly.

  8. Encourage Human-AI Collaboration: Design assignments and projects that require students to work alongside LLMs, leveraging AI capabilities while applying their own critical thinking and creativity. This approach can help students understand how to effectively integrate AI tools into their learning and problem-solving processes.

  9. Regular Assessment of LLM Impact: Conduct ongoing evaluations of how LLM use affects student learning outcomes, critical thinking skills, and overall academic performance. Use these assessments to refine teaching strategies and LLM integration in educational settings.

  10. Promote Interdisciplinary Approaches: Encourage the integration of LLMs across different subject areas, highlighting how critical thinking skills apply across disciplines. This approach can help students develop a more holistic understanding of AI’s role in various fields of study.

6. Recommendations for Enhancing Accessibility with LLMs

To maximize the potential of Large Language Models (LLMs) in enhancing accessibility while mitigating their limitations, the following recommendations are proposed:

6.1 Addressing Bias in LLMs

  1. Employ Diverse and Representative Training Datasets: Ensure that the data used to train LLMs includes a wide range of perspectives, experiences, and languages, particularly those related to disability and accessibility [63]. This approach can help reduce inherent biases and improve the relevance of LLM outputs for diverse user groups.

  2. Implement De-biasing Strategies: Utilize techniques such as fine-tuning with human preferences and post-generation self-diagnosis to identify and mitigate biases in LLM outputs. Regular audits and updates to these strategies should be conducted to address emerging biases.

6.2 Ensuring Ethical Development

  1. Involve Individuals with Disabilities in Design and Testing: Actively engage people with disabilities throughout the development process of LLM applications to ensure their needs are adequately addressed [99]. This inclusive approach can lead to more effective and user-friendly accessibility solutions.

  2. Establish Ethical Guidelines: Develop comprehensive guidelines for the ethical use of LLMs in accessibility applications. These guidelines should address issues such as data privacy, transparency, and the potential impact on user autonomy.

  3. Appoint Ethics Officers: Designate ethics officers to oversee the development and deployment of LLM-based accessibility solutions, ensuring adherence to ethical guidelines and best practices [100].

6.3 Improving Transparency and Accountability

  1. Develop Explainable AI Techniques: Invest in research and development of methods to enhance the interpretability of LLM outputs, particularly in accessibility applications [101]. This can help users and developers understand the reasoning behind LLM-generated content and identify potential errors or biases.

  2. Regular Auditing: Implement systematic auditing processes to identify and rectify instances of bias or inaccuracy in LLM systems [102]. These audits should be conducted by diverse teams including accessibility experts and individuals with disabilities.

6.4 Expanding Access to LLM Technologies

  1. Reduce Costs: Develop strategies to lower the cost of LLM-based accessibility solutions, making them more accessible to underprivileged communities and smaller organizations [42]. This could include open-source initiatives, subsidized access programs, or tiered pricing models.

  2. Increase Awareness: Launch educational campaigns to raise awareness about the benefits of LLMs and assistive technologies among people with disabilities, their families, and caregivers [103]. These campaigns should provide clear, accessible information on how to effectively use and benefit from LLM-based tools.

6.5 Enhancing Education and Training

  1. Develop Accessibility-Focused Curricula: Create educational programs that focus on the development and implementation of LLM-based accessibility solutions. These programs should cover technical aspects as well as ethical considerations and user-centered design principles.

  2. Provide Training for Professionals: Offer training programs for healthcare providers, educators, and other professionals working with individuals with disabilities on how to effectively integrate LLM-based tools into their practice.

6.6 Fostering Collaboration and Knowledge Sharing

  1. Establish Research Partnerships: Encourage collaboration between academic institutions, technology companies, and disability advocacy organizations to advance research in LLM-based accessibility solutions.

  2. Create Open Platforms: Develop open platforms for sharing best practices, research findings, and user feedback on LLM accessibility applications. This can accelerate innovation and improvement in the field.

6.7 Implementing Robust Privacy Protections

  1. Develop Privacy-Preserving Techniques: Invest in research and development of privacy-preserving techniques for LLM applications, particularly those handling sensitive user data in accessibility contexts.

  2. Transparent Data Handling Policies: Implement clear and accessible policies regarding data collection, storage, and use in LLM-based accessibility tools. Ensure that users have control over their data and understand how it is being used.

6.8 Promoting Inclusive Design Principles

  1. Adopt Universal Design Approaches: Integrate universal design principles into the development of LLM-based accessibility solutions to ensure they benefit the widest possible range of users.

  2. Customization Options: Provide robust customization options in LLM-based tools to allow users to tailor the experience to their specific needs and preferences.

6.9 Continuous Evaluation and Improvement

  1. User Feedback Mechanisms: Implement robust feedback mechanisms to continuously gather input from users with disabilities on the effectiveness and usability of LLM-based accessibility tools.

  2. Iterative Development: Adopt an iterative development approach, regularly updating and improving LLM-based accessibility solutions based on user feedback, technological advancements, and emerging accessibility standards.

6.10 Addressing the Digital Divide

  1. Infrastructure Development: Support initiatives to improve digital infrastructure in underserved areas, ensuring that individuals with disabilities in these communities can benefit from LLM-based accessibility tools.

  2. Device Access Programs: Develop programs to provide accessible devices and internet connectivity to individuals with disabilities who may not have the resources to access LLM-based technologies.

By implementing these recommendations, we can work towards a future where LLMs significantly enhance accessibility for people with disabilities while addressing the ethical, technical, and societal challenges associated with these powerful technologies.

7. Conclusion

Large Language Models (LLMs) have emerged as transformative tools with significant implications for accessibility, both in terms of assisting people with disabilities and broadening access to informed studies. Their capabilities in natural language processing, multimodal interaction, and personalization offer unprecedented opportunities to enhance the lives of individuals with disabilities and democratize access to knowledge.

In the realm of accessibility for people with disabilities, LLMs have demonstrated potential in improving assistive technologies, personalizing user experiences, and broadening access to information. They have shown promise in enhancing screen readers, voice-controlled systems, and augmentative and alternative communication devices. The ability of LLMs to adapt to individual needs and preferences offers a level of personalization that can significantly improve the digital experience for users with diverse abilities.

However, the implementation of LLMs in accessibility applications is not without challenges. Issues of bias, fallibility, and ethical concerns need to be carefully addressed. The potential for LLMs to perpetuate or amplify existing biases, particularly in relation to disability, highlights the need for diverse and representative training data and ongoing monitoring. Privacy concerns, especially in the context of sensitive communication data, underscore the importance of robust data protection measures.

In terms of broadening access to informed studies, LLMs have shown great potential in democratizing knowledge and facilitating research. Tools like You.com’s ARI, which leverage existing publications for analysis, offer a more reliable approach to AI-assisted research compared to ‘raw’ LLM use. These tools can streamline the research process, make complex scientific knowledge more accessible to non-specialists, and break down language barriers through multilingual capabilities.

The fallibility of LLMs, while often seen as a limitation, also presents opportunities for enhancing critical thinking and learning. When users are aware of the potential inaccuracies in LLM outputs, they are encouraged to critically evaluate information, cross-check sources, and engage in deeper analysis. This process can foster a more rigorous approach to learning and research, ultimately enhancing the quality of informed studies.

However, the risk of over-reliance on LLMs leading to a decline in independent thinking skills and the propagation of misinformation cannot be ignored. It is crucial to implement strategies that promote critical thinking, enhance feedback literacy, and encourage responsible use of AI tools in educational and research settings.

To fully realize the potential of LLMs in enhancing accessibility and broadening access to informed studies, a multifaceted approach is necessary. This includes addressing biases in LLMs, ensuring ethical development practices, improving transparency and accountability, expanding access to LLM technologies, and fostering collaboration between technology developers, researchers, and the disability community.

As we move forward, it is essential to continue research and development in this field, always keeping in mind the diverse needs of users with disabilities and the importance of promoting critical thinking in the age of AI. By addressing the challenges and leveraging the opportunities presented by LLMs, we can work towards a more inclusive and informed society where technology serves to empower all individuals, regardless of their abilities or background.

References

  1. 5 Best Large Language Models (LLMs) in April 2025. https://www.unite.ai
  2. A Comprehensive Guide to LLM Development in 2025. https://www.turing.com
  3. Top 9 Large Language Models as of April 2025 | Shakudo. https://www.shakudo.io
  4. LLM Trends 2025: A Deep Dive into the Future of Large Language Models. https://prajnaaiwisdom.medium.com
  5. 5 Best Large Language Models (LLMs) in April 2025. https://www.unite.ai
  6. 5 Best Large Language Models (LLMs) in April 2025. https://www.unite.ai
  7. A Comprehensive Guide to LLM Development in 2025. https://www.turing.com
  8. Comprehensive Guide to Large Language Model (LLM) Security | Lakera â Protecting AI teams that disrupt the world.. https://www.lakera.ai
  9. Generative AI Ethics in 2025: Top 6 Concerns. https://research.aimultiple.com
  10. AI on AI: Popular Large Language Models Weigh In on What’s Next for AI in 2025 | College of Computing. https://www.cc.gatech.edu
  11. 5 Best Large Language Models (LLMs) in April 2025. https://www.unite.ai
  12. Large Language Model Statistics And Numbers (2025) - Springs. https://springsapps.com
  13. Generative AI Ethics in 2025: Top 6 Concerns. https://research.aimultiple.com
  14. 8 Ethical Considerations of Large Language Models (LLM) Like GPT-4. https://www.unite.ai
  15. Large Language Models: What You Need to Know in 2025 | HatchWorks AI. https://hatchworks.com
  16. LLM Security: Challenges & Best Practices. https://www.lasso.security
  17. LLM Security: Challenges & Best Practices. https://www.lasso.security
  18. 8 Ethical Considerations of Large Language Models (LLM) Like GPT-4. https://www.unite.ai
  19. LLM Limitations, Risks, Statistics and Future. https://masterofcode.com
  20. LLM Limitations, Risks, Statistics and Future. https://masterofcode.com
  21. Large Language Models: What You Need to Know in 2025 | HatchWorks AI. https://hatchworks.com
  22. Superagency in the workplace: Empowering people to unlock AIâs full potential. https://www.mckinsey.com
  23. You.com Launches ARI, an AI Research Tool Processing 400+ Sources Simultaneously. https://www.aibase.com
  24. You.com Launches ARI, an AI Research Tool Processing 400+ Sources Simultaneously. https://www.aibase.com
  25. Introducing ARI: The First Professional-Grade Research Agent for Business. https://home.you.com
  26. You.com unveils AI research agent that processes 400+ sources at once | VentureBeat. https://venturebeat.com
  27. You.com Launches ARI, an AI Research Tool Processing 400+ Sources Simultaneously. https://www.aibase.com
  28. You.com Launches ARI, an AI Research Tool Processing 400+ Sources Simultaneously. https://www.aibase.com
  29. You.com Launches ARI, an AI Research Tool Processing 400+ Sources Simultaneously. https://www.aibase.com
  30. You.com Launches ARI, an AI Research Tool Processing 400+ Sources Simultaneously. https://www.aibase.com
  31. Introducing ARI: The First Professional-Grade Research Agent for Business. https://home.you.com
  32. Introducing ARI: The First Professional-Grade Research Agent for Business. https://home.you.com
  33. ARI vs. ChatGPT Deep Research vs. Google Deep Research: Why Businesses Are Choosing ARI | You.com. https://you.com
  34. Introducing ARI: The First Professional-Grade Research Agent for Business. https://home.you.com
  35. Introducing ARI: The First Professional-Grade Research Agent for Business. https://home.you.com
  36. LibGuides: Integration of AI tools into your research: Scite. https://libguides.library.arizona.edu
  37. R Discovery vs Scite AI: What is the Difference? | R Discovery. https://discovery.researcher.life
  38. McMaster LibGuides: A Guide to AI Tools for Research: Scite. https://libguides.mcmaster.ca
  39. Top 7 AI Tools for Research in 2025 (Compared) | Paperpal. https://paperpal.com
  40. LibGuides: Artificial Intelligence and Library Services: AI Research Tools. https://libguides.niu.edu
  41. Semantic Scholar - Wikipedia. https://en.wikipedia.org
  42. Semantic Scholar - Wikipedia. https://en.wikipedia.org
  43. Semantic Scholar - Easy With AI. https://easywithai.com
  44. Semantic Scholar - Wikipedia. https://en.wikipedia.org
  45. AI Literature Review, Access 115M+ Academic Research Papers for Free | R Discovery by Editage. https://www.editage.com
  46. R Discovery Review: Is it the Best AI Tool for Literature Search? | Paperpal. https://paperpal.com
  47. AI Literature Review, Access 115M+ Academic Research Papers for Free | R Discovery by Editage. https://www.editage.com
  48. These AI tools could help boost your academic research. https://www.euronews.com
  49. The Best 7 Research AI Tools You Can Use for Your Research Field in 2024 [video review]. https://blog.hslu.ch
  50. Consensus Vs Scite AI: Which AI Research Tool Fits Your Needs?. https://doctoraimd.com
  51. Guides: Artificial Intelligence (Generative) Resources: AI Tools for Research. https://guides.library.georgetown.edu
  52. These AI tools could help boost your academic research. https://www.euronews.com
  53. Your AI UX Intern: Meet Ari. https://www.nngroup.com
  54. Databases & Subject Guides: AI Literacy : Tool Comparison. https://guides.libs.uga.edu
  55. Guides: AI in Academic Research and Writing: AI Tools for Academic Research & Writing. https://info.library.okstate.edu
  56. You.com Launches ARI, an AI Research Tool Processing 400+ Sources Simultaneously. https://www.aibase.com
  57. Introducing ARI: The First Professional-Grade Research Agent for Business. https://home.you.com
  58. Your AI UX Intern: Meet Ari. https://www.nngroup.com
  59. Key Strategies to Minimize LLM Hallucinations: Expert Insights. https://www.turing.com
  60. Key Strategies to Minimize LLM Hallucinations: Expert Insights. https://www.turing.com
  61. Key Strategies to Minimize LLM Hallucinations: Expert Insights. https://www.turing.com
  62. Top 10 Cons & Disadvantages of Large Language Models (LLM). https://projectmanagers.net
  63. Easy Problems That LLMs Get Wrong. https://arxiv.org
  64. Easy Problems That LLMs Get Wrong. https://arxiv.org
  65. Easy Problems That LLMs Get Wrong. https://arxiv.org
  66. Top 10 Cons & Disadvantages of Large Language Models (LLM). https://projectmanagers.net
  67. Are LLMs actually good for learning? - AI & SOCIETY. https://link.springer.com
  68. Are LLMs actually good for learning? - AI & SOCIETY. https://link.springer.com
  69. The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review | Smart Learning Environments | Full Text. https://slejournal.springeropen.com
  70. The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review | Smart Learning Environments | Full Text. https://slejournal.springeropen.com
  71. Risk of LLMs in Education. https://publish.illinois.edu
  72. The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review | Smart Learning Environments | Full Text. https://slejournal.springeropen.com
  73. The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review | Smart Learning Environments | Full Text. https://slejournal.springeropen.com
  74. The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review | Smart Learning Environments | Full Text. https://slejournal.springeropen.com
  75. LLM Evaluation in the Age of AI: What’s Changing? The Paradigm Shift in Measuring AI Model Performance - Magnimind Academy. https://magnimindacademy.com
  76. Are LLMs actually good for learning? - AI & SOCIETY. https://link.springer.com
  77. The Impact of LLMs on Learning and Education. https://medium.com
  78. Are LLMs actually good for learning? - AI & SOCIETY. https://link.springer.com
  79. Understanding the Unexpected: The Adverse Influence of Large Language Models on Critical Thinking and Professional Skepticism in Accounting Education by Ihsan Manshur Putra, Fauziah Istiqomah Abdunnafi :: SSRN. https://papers.ssrn.com
  80. Learning About LLMs and How the Human Mind Works: A Holistic Approach to Critical Thinking in the Classroom Using Generative AI | by YogaMac.ai | Medium. https://medium.com
  81. Don’t Let Students Outsource Critical Thinking to AI » Thinking Maps. https://www.thinkingmaps.com
  82. Investigating the use of chatGPT as a tool for enhancing critical thinking and argumentation skills in international relations debates among undergraduate students - Smart Learning Environments. https://slejournal.springeropen.com
  83. Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education. https://arxiv.org
  84. Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education. https://arxiv.org
  85. Beyond Answers: How LLMs Can Pursue Strategic Thinking in Education. https://arxiv.org
  86. LLM-based collaborative programming: impact on students’ computational thinking and self-efficacy - Humanities and Social Sciences Communications. https://www.nature.com
  87. Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to Boost for Reasoning. https://arxiv.org
  88. Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to Boost for Reasoning. https://arxiv.org
  89. Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to Boost for Reasoning. https://arxiv.org
  90. CRITIC: Large Language Models Can Self-Correct with…. https://openreview.net
  91. ChatGPT for good? On opportunities and challenges of large language models for education. https://www.sciencedirect.com
  92. Frontiers | Embracing LLM Feedback: the role of feedback providers and provider information for feedback effectiveness. https://www.frontiersin.org
  93. The two key steps to promoting responsible use of LLMs | THE Campus Learn, Share, Connect. https://www.timeshighereducation.com
  94. 3 Strategies to Reduce LLM Hallucinations. https://www.vellum.ai
  95. Key Strategies to Minimize LLM Hallucinations: Expert Insights. https://www.turing.com
  96. How can LLMs be leveraged to improve accessibility in techn…. https://interviewdb.com
  97. The Impact of AI and Machine Learning on Advancing Digital Accessibility Features. https://www.grackledocs.com
  98. Stephanie Valencia² Explores How Large Language Models Can Accommodate People with Disabilities - College of Information (INFO). https://ischool.umd.edu
  99. Stephanie Valencia² Explores How Large Language Models Can Accommodate People with Disabilities - College of Information (INFO). https://ischool.umd.edu
  100. How can LLMs be leveraged to improve accessibility in techn…. https://interviewdb.com
  101. How can LLMs be leveraged to improve accessibility in techn…. https://interviewdb.com
  102. Researchers teach LLMs to solve complex planning challenges. https://news.mit.edu
  103. How can LLMs be leveraged to improve accessibility in techn…. https://interviewdb.com
  104. Understanding Accessibility. https://www.weaccess.ai
  105. Turning manual web accessibility success criteria into automatic: an LLM-based approach - Universal Access in the Information Society. https://link.springer.com
  106. Can LLMs spot accessibility issues?. https://blog.scottlogic.com
  107. Can LLMs spot accessibility issues?. https://blog.scottlogic.com
  108. AI language models show bias against people with disabilities, study finds | Penn State University. https://www.psu.edu
  109. AI language models show bias against people with disabilities, study finds | Penn State University. https://www.psu.edu
  110. Social Biases in NLP Models as Barriers for Persons with Disabilities. https://ar5iv.labs.arxiv.org
  111. Social Biases in NLP Models as Barriers for Persons with Disabilities. https://ar5iv.labs.arxiv.org
  112. LLM Challenges in Development: Key Insights. https://www.labellerr.com
  113. Challenges Facing LLM Tools and Solutions. https://medium.com
  114. Stephanie Valencia² Explores How Large Language Models Can Accommodate People with Disabilities - College of Information (INFO). https://ischool.umd.edu
  115. The Impact of AI and Machine Learning on Advancing Digital Accessibility Features. https://www.grackledocs.com
  116. The Impact of AI in Advancing Accessibility for Learners with Disabilities | EDUCAUSE Review. https://er.educause.edu
  117. No, large language models aren’t like disabled people (and it’s problematic to argue that they are). https://medium.com
  118. No, large language models aren’t like disabled people (and it’s problematic to argue that they are). https://medium.com
  119. Understanding Accessibility. https://www.weaccess.ai
  120. Understanding Accessibility. https://www.weaccess.ai
  121. The Impact of AI and Machine Learning on Advancing Digital Accessibility Features. https://www.grackledocs.com
  122. What are the Key Benefits of Assistive Technology?. https://reciteme.com
  123. How can AI Large Language Model(LLM) / Chatbot improve Education Equity? [2025 DEI Resources] | Diversity for Social Impact. https://diversity.social
  124. LLM Challenges in Development: Key Insights. https://www.labellerr.com
  125. What are Ethics and Bias in LLMs?. https://www.appypie.com
  126. De-biasing LLMs: A Comprehensive Framework for Ethical AI. https://www.appypie.com
  127. How to mitigate bias in LLMs (Large Language Models) - Hello Future Orange. https://hellofuture.orange.com
  128. Challenges Facing LLM Tools and Solutions. https://medium.com
  129. What are Ethics and Bias in LLMs?. https://www.appypie.com
  130. How can we promote access to assistive technology for individuals with disabilities in low- and middle-income settings? - DEP. https://www.disabilityevidence.org
  131. Open Source LLMs and the Democratization of AI: Empowering the Future. https://www.alphasquarelabs.com
  132. Open Source LLMs and the Democratization of AI: Empowering the Future. https://www.alphasquarelabs.com
  133. Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science This paper is published in AI2024: Advances in Artificial Intellgience, DOI: 10.1007/978-981-96-0348-0_3. https://arxiv.org
  134. Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science This paper is published in AI2024: Advances in Artificial Intellgience, DOI: 10.1007/978-981-96-0348-0_3. https://arxiv.org
  135. Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science This paper is published in AI2024: Advances in Artificial Intellgience, DOI: 10.1007/978-981-96-0348-0_3. https://arxiv.org
  136. Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science This paper is published in AI2024: Advances in Artificial Intellgience, DOI: 10.1007/978-981-96-0348-0_3. https://arxiv.org
  137. Best 10 Large Language Models in Healthcare in 2025. https://research.aimultiple.com
  138. What Are Large Language Models (LLMs)? | IBM. https://www.ibm.com
  139. A systematic review of AI, VR, and LLM applications in special education: Opportunities, challenges, and future directions | Education and Information Technologies. https://link.springer.com
  140. A systematic review of AI, VR, and LLM applications in special education: Opportunities, challenges, and future directions | Education and Information Technologies. https://link.springer.com
  141. Are LLMs unlikely to be useful to generate any scientific discovery?. https://ai.stackexchange.com
  142. What are Ethics and Bias in LLMs?. https://www.appypie.com
  143. What are Ethics and Bias in LLMs?. https://www.appypie.com
  144. How LLMs could widen digital divide. https://www.thehindubusinessline.com
  145. How LLMs could widen digital divide. https://www.thehindubusinessline.com
  146. Consequences of the Digital Divide in Education - Connecting the Unconnected. https://ctu.ieee.org
  147. The Role of Language Models in Education: Transforming Learning with AI. https://www.linkedin.com
  148. Are LLMs actually good for learning? - AI & SOCIETY. https://link.springer.com