Farhan Ali

Preferred name
Farhan Ali
Email
farhan.ali@nie.edu.sg
Department
Learning Sciences and Assessment (LSA)
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2021 - 20232
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    Technologies, neuroscience and education
    (Pearson, 2021)
    Farhan Ali 

    Aided by rapid developments in technologies, there has been an explosion of knowledge about the brain in the past two decades. This neuroscientific knowledge is increasingly penetrating the field of education. This chapter covers topics at the intersection of technologies, neuroscience and education. First, a brief overview is given of the technologies that have enabled discoveries about the brain relevant for learning. Second, the chapter elaborates on how a better understanding of the brain has made pertinent impacts for practice and policy. Finally, the chapter discusses emerging technologies for the future of education. While most work has been done overseas, relevance to the local classroom contexts will be made appropriately.

    Neuroscience and education have traditionally been separate disciplines with different, sometimes conflicting, underlying philosophies and approaches. However, intersections between the two are increasingly being developed. Leslie Hart, a psychologist, argues that designing educational experiences without an understanding of the brain is like designing a glove without an understanding of the human hand. This chapter addresses this important need to optimise learning experiences using neuroscience knowledge.

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    Supporting self-directed learning and self-assessment using TeacherGAIA, a generative AI chatbot application: Learning approaches and prompt engineering
    (Taylor & Francis, 2023)
    Farhan Ali 
    ;
    Choy, Doris 
    ;
    Divaharan, Shanti 
    ;
    Tay, Hui Yong 
    ;
    Chen, Wenli 
    Self-directed learning and self-assessment require student responsibility over learning needs, goals, processes, and outcomes. However, this student-led learning can be challenging to achieve in a classroom limited by a one-to-many teacher-led instruction. We, thus, have designed and prototyped a generative artificial intelligence chatbot application (GAIA), named TeacherGAIA, that can be used to asynchronously support students in their self-directed learning and self-assessment outside the classroom. We first identified diverse constructivist learning approaches that align with, and promote, student-led learning. These included knowledge construction, inquiry-based learning, self-assessment, and peer teaching. The in-context learning abilities of large language model (LLM) from OpenAI were then leveraged via prompt engineering to steer interactions supporting these different learning approaches. These interactions contrasted with ChatGPT, OpenAI’s chatbot which by default engaged in the traditional transmissionist mode of learning reminiscent of teacher-led instruction. Preliminary design, prompt engineering and prototyping suggested fidelity to the learning approaches, cognitive guidance, and social-emotional support, all of which were implemented in a generative AI manner without pre-specified rules or “hard-coding”. Other affordances of TeacherGAIA are discussed and future development outlined. We anticipate TeacherGAIA to be a useful application for teachers in facilitating self-directed learning and self-assessment among K-12 students.
    Scopus© Citations 7  105