IV. Teaching and Learning in the Age of Artificial Intelligence

Introduction

Artificial intelligence has the potential to enhance teaching and learning. In schools and on campuses across the country, AI is gaining traction and challenging traditional teaching methods. These tools, when used safely and appropriately, may boost student motivation through tailored learning paths, provide greater access to educational content for all students, save educators time that then can be used to provide students with more engaging experiences, and more. This section outlines AI’s potential benefits to teaching and learning and specific implications for students and educators with disabilities.

A. The Potential Benefits of Artificial Intelligence in Teaching and Learning

There are many potential benefits to integrating AI tools into classrooms, schools, and campuses. Some of these tools are tailored for the education market, while others are general tools also used by the public. Here, we provide an overview of some of the primary areas in which AI is emerging and examples of specific tools that are in use:

Lesson Planning for Educators: With artificial intelligence, lesson planning may be streamlined, freeing up valuable time that educators can redirect toward meaningful instruction, fostering discussions, and facilitating reflective learning experiences for both students and educators.

Educators can use AI tools, such as Magic School, to generate customizable lesson plans that align with their curriculum and standards. These lesson plans can also be differentiated to suit various student needs.

Personalized Learning: Many AI tools can customize content to meet the needs of individual students by adjusting lessons for a slower or faster pace, providing activities that consider learning preferences, and integrating areas of interest into lessons. Although AI tools focused on personalized learning may be useful for addressing learning gaps and increasing student engagement, limited studies have reported mixed results.

An example of personalized learning is LitLab, which supports K–2 educators in developing personalized decodables to help early readers build knowledge and vocabulary. Students can also create their own illustrated stories based on their interests.

Data-Driven Insights: By utilizing AI’s ability to analyze vast amounts of data, educators can glean insights into student learning patterns and skill levels. This data can help educators identify areas where students struggle and then adjust their teaching strategies.

An example of an AI tool that provides data-driven insights is Writable, which supports K–12 educators. This tool can save educators time by analyzing student writing, allowing more time for instruction and engagement.

Engagement and Motivation: AI-driven educational games and simulations can make learning more engaging for students, potentially increasing motivation and participation. These interactive tools can also help illustrate complex concepts in accessible ways.

Simulations such as PhET, Mission US, and Cornucopia use AI to create immersive experiences that allow students to make decisions, try out new ideas, and explore complex real-world issues.

Educator Support and Development: AI can assist in coaching and professional learning by offering educators personalized learning opportunities, insights into their teaching practices, and suggestions for improvement.

The International Standards for Technology in Education (ISTE) and ASCD are developing StretchAI, an AI coach just for teachers. This platform promises to deliver personalized tips and strategies to create more inclusive learning environments.

AI also has the potential to enhance assessment practices. Some AI tools show promise and can potentially save time in grading and evaluating student work; for example, code.org is testing an AI Teaching Assistant that reviews student projects based on an educator-developed rubric and recommends scores, along with evidence for each recommendation. Yet, educators and researchers have expressed concerns about AI assessment tools, citing problems with algorithmic bias and inaccurate or nonsensical outputs, issues we explore further in Section V.C.4.b.

To ensure that these changes are equitable and protect the privacy and safety of students, a reevaluation of current assessment practices and uses is the first step in developing a plan to implement AI-enhanced assessment methodologies. If developed and implemented ethically and with guidance from educators, then AI may transform assessment from a one-size-fits-all model of standardized testing to more responsive and individualized assessment practices. Below are some ways in which AI can enhance student assessment:

Faster Feedback: AI-enhanced assessment systems can analyze large amounts of student data quickly to provide real-time feedback, predict learning outcomes, and identify areas of growth and next steps.
Competency and Task Development: AI assessment tools have the potential to assist with developing competencies and tasks, providing greater attention to critical thinking skills, understanding new ways to align competencies and tasks, and automating the development of learning materials aligned to competencies and tasks.
Test Assembly and Delivery: AI assessment tools can make the assessment assembly and delivery process more efficient through automation. Increased efficiency also provides greater opportunities for personalizing assessments and including a more extensive range of tasks.

This transition requires not only technological systems but also a cultural shift in how student achievement is measured. Educators, administrators, and policymakers must engage in collaborative conversations that lead to improved assessment practices. Moreover, these enhancements may help educators to better understand student's knowledge, skills, and abilities, moving beyond traditional measures to embrace a more holistic view of learning.

15 Please note that NEA does not endorse the use of any specific AI tools or resources.
16 Frank Kehoe, "Leveraging Generative AI Tools for Enhanced Lesson Planning in Initial Teacher Education at Post Primary," Irish Journal of Technology Enhanced Learning 7, no. 2 (2023), https://doi.org/10.22554/ijtel.v7i2.124.
17 Olga Tapalova and Nadezhda Zhiyenbayeva, "Artificial Intelligence in Education: AIEd for Personalised Learning Pathways," Electronic Journal of e-Learning 20, no. 5 (2023), https://academic-publishing.org/index.php/ejel/article/view/2597.
18 Dorottya Demszky et al., "Can Automated Feedback Improve Teachers’ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial in a Large-Scale Online Course," Educational Evaluation and Policy Analysis OnlineFirst (2023), https://arxiv.org/abs/2005.02431; Ekaterina Kochmar et al., "Automated Personalized Feedback Improves Learning Gains in An Intelligent Tutoring System," in Artificial Intelligence in Education AIED 2020, Lecture Notes in Computer Science (Springer International Publishing, 2020).
19 Demszky et al., "Can Automated Feedback Improve Teachers’ Uptake of Student Ideas? Evidence From a Randomized Controlled Trial in a Large-Scale Online Course."
20 Abdullah Alenezi, "Teacher Perspectives on AI-Driven Gamification: Impact on Student Motivation, Engagement, and Learning Outcomes," Information Technologies and Learning Tools 97, no. 5 (2023), https://doi.org/10.33407/itlt.v97i5.5437; Ching-Huei Chen and Ching-Ling Chang, "Effectiveness of AI-assisted game-based learning on science learning outcomes, intrinsic motivation, cognitive load, and learning behavior," Education and Information Technologies IF 3.666 (2024), https://doi.org/10.1007/s10639-024-12553-x.
21 Jasmin Cowin et al., "Accelerating Higher Education Transformation: Simulation-Based Training and AI Coaching for Educators-in-Training," in Towards a Hybrid, Flexible and Socially Engaged Higher Education ICL 2023, Lecture Notes in Networks and Systems, vol 899 (Springer Nature Switzerland, 2024); Patty Hagan, "Using AI to Support Teacher Coaching," ISTE, August 15, 2023, https://iste.org/blog/using-ai-to-support-teacher-coaching; Stephen Noonoo, "Improving Your Teaching With an AI Coach," Edutopia, December 1, 2023, https://www.edutopia.org/article/improving-your-teaching-ai-coach/.
22 André A. Rupp and Will Lorié, "Ready or Not: AI is Changing Assessment and Accountability," Center for Assessment, April 19, 2023, https://www.nciea.org/blog/ready-or-not-ai-is-changing-assessment-and-accountability/.
23 For more on AI grading and evaluation tools, see: Cristian D. González-Carrillo et al., "Automatic Grading Tool for Jupyter Notebooks in Artificial Intelligence Courses," Sustainability 13, no. 21 (2021), https://doi.org/10.3390/su132112050; Erin Hall, Mohammed Seyam, and Daniel Dunlap, "Identifying Usability Challenges in AI-Based Essay Grading Tools," in Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium, and Blue Sky, Communications in Computer and Information Science (Springer Nature Switzerland, 2023); Stephen M. Rutner and Rebecca A. Scott, "Use of Artificial Intelligence to Grade Student Discussion Boards: An Exploratory Study," Information Systems Education Journal 20, no. 4 (2022), http://files.eric.ed.gov/fulltext/EJ1358299.pdf.
24 The Task Force acknowledges that this is not a comprehensive list. As AI tools are implemented and further research is conducted, educators will gain more understanding of the benefits AI can bring to student assessment.
25 Mary Richardson and Rose Clesham, "Rise of the Machines? The Evolving Role of AI Technologies in High-Stakes Assessment," London Review of Education 19, no. 1 (2021), https://doi.org/10.14324/LRE.19.1.09.
26 André A. Rupp and Will Lorié, Implications of Advances in Artificial Intelligence (AI) for 10 Areas of Work in Educational Assessment and Accountability, Center for Assessment (2023), https://www.nciea.org/wp-content/uploads/2023/04/Implications-of-Advances-of-AI-PDF-Rupp-Lorie-April-2023.pdf.
27 Rupp and Lorié, Implications of Advances in Artificial Intelligence (AI) for 10 Areas of Work in Educational Assessment and Accountability.

B. The Potential Benefits of Artificial Intelligence for Students and Educators with Disabilities

Artificial intelligence-enabled systems offer many potential opportunities for disability inclusion and independence, revolutionizing assistive technologies. AI technology must not conform to a purely ableist and privileged standard that neither serves nor adapts to the educational needs of students with disabilities. User cases that aid in the development of effective AI tools in education must be based on a range of disabilities (i.e., learning disabilities, hearing impairments, visual impairments, etc.).

Within the disability community, there are many different disability types that may inform how a student interacts with AI. The Individuals with Disabilities Education Act (IDEA) denotes 13 different disability categories and the varying ways they manifest in student presentation. For example, under IDEA, a student with a traumatic brain injury may present with “impairments in one or more areas, such as cognition; language; memory; attention; reasoning; abstract thinking; judgment; problem-solving; sensory, perceptual, and motor abilities; psychosocial behavior; physical functions; information processing; and speech.” This, of course, informs how a student may perceive and/or interact with AI. Alternatively, a student with an orthopedic impairment, such as an amputee, may have very different needs, considerations, and recommendations for the development and deployment of artificial intelligence. Given federal laws' requirements for educators to meet the individualized needs of students with varying disabilities and degrees of support needs, it is critical for us to consider the disability population and the multitude of accessibility needs at the forefront of the conversation.

Artificial intelligence tools can empower individuals with disabilities to meet personal needs, enhance personal mobility, and support communication through eye-tracking and voice-recognition software, among other benefits. The adaptive nature of AI provides a pathway to address specific individual needs, significantly expanding possibilities for reasonable accommodations for both students and educators. AI is commonly used in inclusive education through adaptive learning platforms and the implementation of speech-to-text and text-to-speech applications.

Educators can leverage AI to strengthen the effectiveness of academic accommodations, providing students with digital tools for notetaking, organizing, planning, and reminders for upcoming assignments. Promising implementations of AI in service of students with disabilities include:

Automated Text Simplification (ATS): Automated processes, such as natural language processing or machine learning, change how texts are worded to make them easier to understand.
Automatic Speech Recognition (ASR): Technology and processes used to recognize and transcribe spoken language.
Object, Scene, and Optical Character Recognition (OCR): The electronic or mechanical conversion of images of typed, handwritten, or printed text into machine-encoded text.

AI can further support people with disabilities and educators working with students with disabilities in various ways:

AI-enabled chatbots can handle simple student queries, allowing educators and specialized instructional support personnel (SISP) to focus on more complex student needs.
AI assists individuals with communication disorders by quickly translating economized phrases into conversational speech-to-text.
AI remediates content, simplifying language into a more accessible, tabular format, including STEM content.
AI adapts reading passages based on the reader’s perceived ability.
AI identifies multiple pathways for students to achieve learning objectives, generating differentiated methods for demonstrating mastery, thereby supporting educators in providing diverse assessment pathways.

While ensuring equitable access for all students and educators is critical, it is similarly important that AI resources are developed for students with diverse learning styles. Along these lines, there are several elements that can be effectively implemented:

Inclusivity in Design: Educators, students, and special education experts should be included in the development of AI resources. AI companies should be developing resources based on needs learned from educator and student feedback. AI tools can also be designed to cater to various learning styles (visual, auditory, kinesthetic, and reading/writing) and intelligences (linguistic, logical-mathematical, spatial, etc.).
Adaptability and Personalization: AI algorithms that can adapt to a student’s learning pace, style, and preferences should be prioritized. Mechanisms for continuous feedback from users should also be incorporated to allow systems to adjust their strategies and content delivery in real-time.
Accessibility: Universal Design for Learning (UDL) Guidelines should be employed to create AI resources that provide multiple means of engagement, representation, action, and expression. AI resources should also comply with accessibility standards to make them usable by students with disabilities, including those who use assistive technologies.
Professional Learning for Educators: Educators should be provided with the necessary professional learning opportunities and resources to effectively integrate AI tools that complement diverse learning styles into their teaching practices.

Ultimately, AI may serve as the foundation for future and inclusive learning environments. However, as we discuss in the next section, great care must be taken in implementing artificial intelligence in education to maximize benefits and mitigate harms.

28 This section includes findings from leading organizations and researchers that have produced comprehensive guidance at the intersection of artificial intelligence and disability rights and inclusion. Primary sources include: the National AI Institute for Exceptional Education, the Center on Inclusive Software for Learning at CAST (CISL), the Center for Democracy & Technology (CDT), and AccessNow.
29 "Sec. 300.8 Child with a Disability—Individuals with Disabilities Education Act," U.S. Department of Education, https://sites.ed.gov/idea/regs/b/a/300.8.
30 For more on the potential benefits of AI for students and teachers with disabilities, see: Anhong Guo et al., "Toward Fairness in AI for People with Disabilities SBG@a Research Roadmap," ACM SIGACCESS Accessibility and Computing, no. 125 (2020), https://doi.org/10.1145/3386296.3386298; Matthew T. Marino et al., "The Future of Artificial Intelligence in Special Education Technology," Journal of Special Education Technology 38, no. 3 (2023), https://doi.org/10.1177/01626434231165977; Sahrish Panjwani-Charania and Xiaoming Zhai, "AI for Students with Learning Disabilities: A Systematic Review," in Uses of Artificial Intelligence in STEM Education, ed. Xiaoming Zhai and Joseph Krajcik (Oxford, UK: Oxford University Press, 2024); Katerina Zdravkova et al., "Cutting-Edge Communication and Learning Assistive Technologies for Disabled Children: An Artificial Intelligence Perspective," Frontiers in Artificial Intelligence 5 (2022), https://doi.org/10.3389/frai.2022.970430.
31 For more about technologies for students with disabilities, see "Accessibility & Inclusive Technology," CAST, 2024, https://www.cast.org/our-work/accessibility-inclusive-technology.
32 "UDL: The UDL Guidelines," CAST, 2024, https://udlguidelines.cast.org/.