Abstract
COVID-19 has presented new challenges for all teachers, especially teachers of students with special needs. Students have struggled with online learning environments with limited opportunities for social interaction and resources. The study investigated a co-design process to help teacher leaders to design inquiry-based learning with the integration of artificial intelligence (AI) technologies. The study utilized the convergent parallel mixed methods approach to analyze both qualitative and qualitative data to capture how teacher leaders introduced inquiry-based learning in the school community. The quantitative analysis revealed shifts in terms of teachers' ability to design inquiry-based projects that integrated AI. The qualitative analysis showed that the growth pattern of teacher leaders might be dependent upon (1) the usefulness of AI, (2) teachers' attitudes about the role of the teacher as a designer, and (3) perceived leadership support. The study provided a co-design process to engage teachers in designing inquiry-based projects during the pandemic times.
TopBackground
Proponents of special education practices have struggled for years to shift away from the deficit thinking model for students with disabilities and advocate for pedagogical changes that support all learners (Harry & Klinger, 2014). Unfortunately, this deficit teaching model, which assumes that students with disabilities are not capable of advanced learning, remains difficult to change because it has become ingrained in teaching practices (e.g., Haberman, 2010; Klehm, 2014). This deficit thinking model also constrains the understanding of learning capacities that fall under the notion of intellective competence: critical literacy and numeracy, problem identification for inquiry processes, and analogical reasoning (Darling-Hammond et al., 2020; Gordon et al., 2005, 2007).
Pandemic posed challenges for the situation as over 1.6 billion learners (91% of the global student population) were impacted as 194 countries ceased normal face-to-face operations. Students with disabilities were the most severely impacted (Bowie, 2020), which intensified inequalities reflecting the lack of resources and teacher preparedness among schools (Basham et al., 2020). Especially now, as COVID continues to interrupt normal school schedules, research on approaches that reduce students' sense of alienation and provide equitable access for students with disabilities have been critical in considering students' mental health, social wellbeing, and academic success (McLaughlin & Vercler, 2020). Therefore, it is critical to understand how to prepare teachers to re-engage students, continuously improving students' academic and social emotional learning outcomes.
Finally, AI education has become a growing field which promotes innovation and development that include the use of computers, machines, and artifacts with human-like intelligence characterized by cognitive abilities, learning, and decision-making capabilities (Garg & Sharma, 2020; Reilly et al., 2020). As our global economy becomes increasingly dependent on highly technical skills, AI has become a key enabler to future job opportunities. Learners need to understand, evaluate, and develop responsible solutions using AI in order to be competitive in the future workforce (Kong et al, 2021). Educators need to develop AI domain knowledge as well as the ability to connect the AI to the real world while designing AI-infused curriculum.
Key Terms in this Chapter
Universal Design for Learning (UDL): A design of curriculum materials, instructional activities, and evaluation procedures that can meet the needs of learners with a wide range of abilities and culturally and linguistically diverse backgrounds.
Educational Technology: Tools, techniques, theories, and methods from multiple knowledge domains to (1) design, develop, and evaluate human and mechanical resources efficiently and effectively to facilitate and leverage all aspects of learning, and consequently, (2) transform education systems and practices.
Artificial Intelligence Technology: A wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.
Deficit Teaching: A primary understanding that dis/ability is a deficit that exists within an individual, and it is therefore something to “cure, accommodate, or endure.” It is fixed, permanent, “owned” by the person.
Artificial Intelligence Literacy: A set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace.
Asset-Based Teaching: An approach seeks to unlock students’ potential by focusing on their talents. Also known as strength-based teaching, this approach contrasts with the more common deficit-based style of teaching which highlights students’ inadequacies.
Inclusion: All students who are educated in general education programs. Inclusion occurs when a student with special learning and/or behavioral needs is educated in the general education program full-time. Essentially, inclusion means that the student with special educational needs is attending the general school program and enrolled in age-appropriate classes for the entirety of the school day.
Convergent Parallel Mixed Methods: A research design that brings the results to combine or compare both quantitative and qualitative data analysis in the study.
Inquiry-Based Learning: An approach that involves students in solving problems with real-world connections to build knowledge in the classrooms.
Inclusive Education: The means to teach all students together in a typical classroom setting, where all students receive instruction that corresponds to their abilities and interests.