Dissertation

Dissertation Prospectus

Enhancing AI Literacy of the Middle School Students in Rural Areas

Research Purpose

This study aims to promote AI Literacy of the middle school students in rural areas. As the uses of AI technologies are becoming more prevalent across disciplines, industries, and geography, social demands for teaching citizens how to critically use AI technologies has increased (DeLyser & Born, 2021). Especially, the younger are considered to be vulnerable to the recent changes with AI, as they are indiscriminately exposed to AI enabled devices and their outputs (Hasse et al., 2019). Consequently, it calls for AI literacy education from the early ages of these young generations. Yet, with being upstaters, AI education in K-12 lacks consensus on what and how to teach (Hrastinski et al., 2019; Tedre et al., 2021). For these reasons, this study will administer design-based research to develop AI curriculum in K-12, assess the impact of the curriculum to middle school students, and generate instructional design principles that can be applied to extended learning environment in K-12.

Theoretical Background

The AI literacy curriculum in this study will be designed based on the competency framework and design considerations suggested by Long and Magerko (2020) and Zhou and her colleagues (2020). The key competencies will center on what AI is, what AI can do, and how AI works including cognitive systems, machine learning, and robotics. The learning components embedded in these competencies will then be integrated with ethical considerations with AI technologies such as privacy, accountability, safety, transparency, and fairness (Fjeld et al., 2020). This discourse will be led into thought-provoking questions of how AI should be used with which learners would develop critical reasoning skills and situational judgment depending on attributes of cases and technologies to be used.

The AI curriculum will be encased in inquiry-based learning where learners are expected to apply multi-faceted knowledge from diverse disciplines while being involved in socially valued, ill-structured problems (Savery, 2006). Inquiry-based learning is reported to be most beneficial at middle school level (Walker & Leary, 2009) and help learners retain knowledge longer and more effectively transfer problem-solving skills in other contexts (Norman & Schmidt, 1992).

Real-life examples of AI and realistic scenarios will be employed in this study to promote understandings of and interests in, mechanisms for AI and what people can do by leveraging them. Learners can benefit from utilizing the environments around them as sources of learning. Hmelo-Silver (2004) claimed that learners’ motivations to learn will be encouraged when problems are authentic and resonate well with learners. This study is targeted to students in rural areas who are recognized to be persistently underrepresented in computer science education (Warner & Fletcher, 2019). The interventions will be designed by taking advantage of the common geographical attributes represented by similar living environments and shared interests of major industries such as agriculture.

Research Questions

This study aims to answer the following questions:

1. Does an inquiry-based AI curriculum influence learners’ conceptual understanding of what AI is and what AI can do?

2. Does an inquiry-based AI curriculum influence learners’ procedural understanding of how AI works?

3. Does an inquiry-based AI curriculum influence learners’ application of critical thinking on how AI should be used?

4. Does an inquiry-based AI curriculum influence learners’ perceptions about learning AI (e.g., self-efficacy, motivations to persist learning)?

Research Plan

Research Design

The proposed study will be conducted following design-based research approach (Collins et al., 2004). From the onset, the interventions will be designed in collaboration with K-12 teachers practicing in rural areas and computer scientists as subject matter experts. The goals, problems, and solutions of the interventions would be mutually identified and negotiated so that they will accommodate the expectations and limitations of the intended learning contexts (Amiel & Reeves, 2008). Through this process, the curriculum will incorporate the needs and voices of the target students and teachers and reflect them in devising pedagogical solutions, leveraging learning theories, empirical studies regarding CS education, and emerging technological tools. Then, the design of the curriculum and tools will undergo iterative cycles of refinement with the aid of pilot testing, and changes will be made to the curriculum to enhance student learning.

This study is intended to produce measurable outcomes by implementing the inquiry-based AI curriculum. The intervention’s pedagogical efficacy will be evaluated by examining students’ conceptual/procedural understandings, critical thinking skills, and attitudes regarding AI technologies. Student achievements will be assessed in an extended experiment by comparing learners’ prior and posterior performances which will be empirical and quantitative in nature. In the end, instructional design principles will be generated that are applicable to similar K-12 learning environments for AI education.

Participants

Middle school students in rural areas will be participating in the study. The participants will be recruited from the volunteered teachers’ classes.

Data Collection & Analysis

For refinement of the curriculum, the pilot-testing will take place in a summer camp in 2022. The classes in the summer camp will be observed by the researcher and the field notes will be kept. In addition, the assessment of learning outcomes will be collected from this site.

Along this line, the extended intervention will gather measures for student learning by employing instruments for cognitive measures and surveys for attitude and values. The field notes will also be collected and qualitatively analyzed to reveal the underlying mechanism of the result to be produced (Sandoval, 2014).

Timeline

• Proposal submitted to committee April 1, 2022

• Proposal defense May 7, 2022

• Data collection complete by December 2022

• Data analysis complete by February 2023

• Manuscript submitted to committee by March 2023

• Defense scheduled April 2023

• Final submission by May 1, 2023