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Work 2: Literature Review

Project Overview

Project Description

Choose an issue, theme or topic within the scope of this course and write a literature review of 2000 words or more, addressing this issue. If you have an idea for your dissertation already, you may wish to choose a topic that intersects with that idea.

The literature review should not merely be descriptive—it should be analytical and critical. However, at the same time it should be a fair representation of the perspectives and voices of a range of people across the field. What are the main issues arising for this issue, theme or topic? The main challenges to be addressed? The questions being asked by the intellectual and practical leaders in relation to this issue?

Sources

Some questions to address in the literature review:

Structure and Process

One possible structure for the literature review might be as follows:

  1. Introduction: how this literature review ties into your experience and research Interests.
  2. The issue, topic or theme: why it is significant and what are the challenges being addressed, as reported by the literature? (You may also wish to structure your literature review around sub-themes, in which case, be sure you cover points 2-6 in each of your subthemes.)
  3. A synthesis of key concepts and theoretical frameworks, as reported by the literature: compare and contrast approaches.
  4. A synthesis of methodologies of research and application employed to address these issues, topics and themes; their strengths and weaknesses, as reported by the literature.
  5. A synthesis of main empirical findings and practical implications.
  6. Gaps in the literature; open questions and where further work is needed.
  7. Conclusion: where this issue is heading, the tasks ahead for people addressing this issue.
  8. References: list sources cited, Including any media.

Checklist for the Literature Review

Icon for The Intersection of AI and Equity

The Intersection of AI and Equity

A Literature Review Examining the Impact of Artificial Intelligence on Equity and Inclusion in Literacy Education

 

Introduction:

The literacies of today, and the pedagogical systems that deliver literacy education, are powered by artificial intelligence. Whether it is an analytics tool that tracks and evaluates a student’s performance, or a learning management system (LMS) that commands the operation of an entire online environment, today’s contemporary literacy pedagogies fuel our capacity to teach, learn, and engage with educational content. Perhaps more importantly, however, these systems have influenced the range in which literacy education can be equitably disseminated, as technology allows for the inclusion of student communities that have otherwise been socially disenfranchised. Thus, I believe that equity in education (or the fair, just and impartial treatment of all students) is the greatest institutional beneficiary of the evolution of intelligent systems, specifically because it has both strengthened teacher capabilities and broadened student access.

As the founder of a non-profit organization that offers STEM and Entrepreneurship education to communities that are disproportionately underrepresented in these fields, and as a community leader who actively engages policymakers to advocate for expansion of educational access through the improvement of best practices, I believe that by utilizing systems intelligence we can grow our shared capacity to make access to literacy education more equitable and inclusive. Our doctoral examination of didactic and non-traditional (or “new”) learning pedagogies suggests that the success of future education will rely on our ability as teachers to incorporate multi-modeled educational systems that are not only multifaceted in delivery (synchronous, asynchronous, ubiquitous, etc.) but are also inclusive with respect to the student communities that they aim to impact. Whether it’s K-12 environments, or adult literacy and vocational education programs, or spaces that engage students whose experience is hindered by economic, social, physical, and cognitive challenges, the impact that intelligent technology can have in expanding access to marginalized student communities I believe will prove to be its greatest achievement.

Thus, for the purposes of this literature review, I will examine how artificial intelligence technology has broadened access to literature education, specifically within the context of Authentic Pedagogies, which are non-traditional systems where learning becomes (as I expressed in the Annotated Bibliography of this assignment portfolio, entitled “The Automation of Authentic and Functional Pedagogies”) “an interactive, experiential, social process and is achieved through the creation and retention of practical knowledge” (Breeden, 2019, p. 1).

Central Concepts:

As my commitments as an educator have helped to shape my research interests, so has my work as a policy advocate helped to inform me about the learning experiences of students who, if not for automated technology, would exist farther outside the social periphery of our education system. Artificial intelligence has aided in creating pedagogical environments that are not only authentic, meaning that they are contemporary alternatives to didactic pedagogies and offer learning experiences that are interactive and experiential, but are more easily accessible to students. In an article by Kandlhofer et al (2016), the authors examine the breadth and comprehensiveness of the impact that automated technology has had within the education sector. The authors suggest that these systems have not only improved how educators teach but what they teach, asserting that our reliance on intelligent systems within our broader society has made “future careers in science and engineering” vastly more important (p. 1). As a result, Kandlhofer et al imply that literacy in disciplines like artificial intelligence, computer science, and technology-related topics will soon be as important as traditional reading and writing skills (p. 1).

Furthermore, as our vernacular in these digital systems evolves, so will the need for educational systems that equitably expand each students’ capacity to connect with these learning opportunities. Viewed through the context of the broader examinations of how technology has positively impacted the state of equity and inclusion in education, and in how artificial intelligence strengthens access for students who otherwise would be excluded from developing fluency in both traditional and innovative fields of study, the sources reviewed suggest that intelligent technology is key in providing students with equitable pathways to achieve in literacy education.

Intelligent Tutoring Systems and Adult Literature Education:

Many of the literacy education technologies that I examine in this review are powered by Intelligent Tutoring Systems (ITS), which essentially are platforms that offer reading and writing instruction as the core function of the operation of their systems. Jacovina and McNamera (2017) consider these platforms to be at the forefront of what has and will continue to spur innovation within the authentic pedagogical systems of literacy education. Their research focuses on 3 distinct forms of systems of instruction: “Writing-focused technologies” which engage students through the practice of sharpening writing skills, “Reading-focused technologies” which aim to improve a student’s reading comprehension and information retention skills, and what they categorize as “Future Technologies” (examples of which we will discuss in the sections to come) which essentially is an analysis of nearly 20 emerging technologies that utilize automation, assessment, and data analytics to improve student experiences and outcomes (pp. 5, 9, 13).

Though much of Kandlhofer et al’s (2016) research focused on systems integration in K-12 education, the authors acknowledge that the impact of artificial intelligence in education should be processed differently across varying age groups; specifically, as the functional literacy requirements within each level of a student’s learning process adjusts according to the obligations of their academic and social environments (p. 1). Examining the shifts in cognitive requirements as students develop along the learning spectrum, and viewed in the context of Jacovina and McNamera’s framing of Intelligent Tutoring Systems, as researchers we can assert that digital automation has influenced both youth and adult literacy education. One such intelligent literacy education platform that engages both K-12 and adult learners is the “Interactive Strategy Training for Active Reading and Thinking” (or iSTART), which according to Johnson, Guerrero, Tighe, and McNamara (2017) aims to combat low literacy levels amongst adolescent youth. Conversely, iSTART-ALL, the system’s “Adult Literacy Learners” platform, was developed to affect the nearly “50% of adults in the US who read below the functional literary level” (p. 125). This, in comparison to international data capturing the average literacy level of adults within the world’s most developed nations, suggests that the United States has a “higher percentage of adults performing at the lowest literacy level” than the combined average of all of the world’s developed countries (p. 125). Below is a video introducing the iSTART learning platform.

Media embedded June 15, 2019

Whereas iSTART operates within the “Reading-focused” and “Writing-focused” categories of Intelligent Tutoring Systems and thus aims to equip K-12 students with the tools to master writing and literary comprehension skills, iSTART-ALL is an instructional technology that incorporates the kinds of functional language and vernacular education that are present in what the authors describe as “deep-level reasoning,” which are represented in the kinds of exchanges that exist in “life-relevant” communication (Kandlhofer et al, 2016; Johnson, Guerrero, Tighe, & McNamara, 2017, pp. 125-126). These include, but are not limited to, family, health, safety, and financial literacies, and are taught through an exercise of “natural language processing algorithms” that can be assessed to determine a student’s progress within the web-based platform (Johnson et al, 2017, p. 127). Thus, the research demonstrates how AI not only effects literacy education across various age demographics but more importantly how it can be developed to impact communities of adults who may be unable to apply appropriate levels of reasoning as a byproduct of low reading and literacy levels.

AI Applications for Students with Impairments:

In the context of creating equitable and inclusive environments where students can explore the breadth of their intellectual capabilities, automated learning systems have also helped to strengthen spaces where students with (among other impairments) physical disabilities can learn and thrive! McCarthy et al (2016), in their case study: “An Artificial Intelligence Tutor: A Supplementary Tool for Teaching and Practicing Braille,” examine the effectiveness of ITS-style systems designed for students with impaired vision. Named the “Braille Tutor,” the technology is an internet-based AI learning tool that relies on a conventional methodology known as “adaptive instruction,” which automatically adjusts the experience of each student according to their performance (pp. 313-314). Doing so allows them to navigate the various modules of the platform in accordance with their demonstrated level of comprehension. Provided is a video of the Braille Tutor platform in action.

Media embedded June 3, 2019

As is the case with iSTART, Braille Tutor encourages mastery and command. Thus, the research would suggest that creating equitable environments for students to engage with learning instruments in authentic pedagogical systems must not only be consistent in terms of the opportunities presented in the learning experience, but in the comparative nature in which student performance is assessed.

Future Technologies - AI in Developing Modalities:

Interestingly, even in considering the system's innovations mentioned, the matter in which artificial intelligence is being used to educate is constantly evolving. As we’ve analyzed, Jacovina and McNamera (2017) forecast a range of ITS usage innovations, from iSTART-2 which extends the original platform’s learning modules to college-age, undergraduate students, to systems like the “Dynamic Support of Contextual Vocabulary Acquisition for Reading” (or DSCoVAR) which is designed to support 4th-8th grade students improve their vocabulary knowledge, to a writing-focused platform named Criterion, which offers automated feedback on student-written essays as a way of assisting teachers with formative assessment (pp. 5,7,9). Moreover, there are also innovations being developed related to the synthesis of contemporary pedagogical methodologies. Specifically, in Xiang’s et al (2018) “Transferring Human Tutor’s Style to Pedagogical Agent,” the authors assess the use of “Pedagogical Agents” (PAs), digital life-like characters whose mannerisms, voice inflections, movements, communication style, and “human-life social cues” have been determined to produce a more effective online learning experience, specifically as they present a hybrid of both didactic, traditional learning and new learning (pp. 1-2). In this case, the authors infer that by including elements like “motion synthesis” and “behavioral simulations,” students are more inclined to respond positively to instruction and evaluation (pp. 2-3). Here’s a video demonstrating the functional elements of pedagogical agent systems behavior.

Media embedded June 15, 2019

Lastly, considering our earlier examination of iSTART-ALL and the adult focuses within the platform’s literacy education program, AI is also being used to develop innovations catered to workforce development. Eguchi’s (2015) “Promoting Innovation Literacy in Education through Educational Robotics” focuses on technologies that embrace Functional Pedagogical philosophy; or those education systems that are designed to prepare students for life outside of the classroom; and examines practices to better equip students for the workforce as opposed to general course instruction (p. 1908).

Challenges, Criticisms, and Gaps in the Literature:

Shared access to educational opportunity is dependent upon our capacity as educators to advance the development of systems that will promote equity and inclusion. However, some scholars challenge the notion that intelligent technology is the answer to improving educational outcomes. Erdogan, Sencer Corlu, and Capraro (2013) argue that while innovations in educational technology, particularly in delivery systems, has been encouraging, there lacks any real data to assert that non-traditional systems drastically improve student performance and administrative efficiency (p. 2). Moreover, while it can be argued that incorporating automated systems into innovative and multidisciplinary fields of education (i.e. STEM and Entrepreneurship) may improve the range of access, improved retention and cognitive performance cannot be determined (p. 2). There are also concerns regarding the reliability of computer-generated data. Anshari et al (2016) argue that in the era of big data and information manipulation, both students and administrators must be vigilant in assuring that the measurements being produced both accurately reflect performance and protect participants from privacy violations (p. 1). It has also been argued that students in contemporary environments perceive their relationship with their instructors differently, as the lack of interpersonal relationship between student and teacher can impact the student’s motivation, the likelihood of them requesting assistance, and most importantly retention (Bawa, 2016; Bowers & Kumar, 2015).

There are also gaps in the literature. A committed examination of the conditions that impact students across the education spectrum must also include an analysis of the student’s experience beyond of the classroom; experiences that can be social, economic, racial, physical, geographical, etc. Therefore, investigating the equitable access to resources also requires investigating the conditions that impact student’s lives at home and in their communities. For instance, access to the resources we’ve examined requires that students have reliable access to (1) a computer (or at least a tablet device), (2) the internet, and (3) the time and availability to engage. If a student lacks any of the three; for personal, social, or economic causes; he or she is unlikely to benefit. The literature also neglects to speak of the cost (if any) of implementing these systems, whether they require institutional licenses or can be purchased by individuals, and the systems requirements for operating them. Lastly, the literature doesn’t consider cultural barriers like language, as (though it wasn’t mentioned specifically) it is likely the most of these systems were designed in English only.

Conclusion:

In closing, the literature suggests that artificial intelligence has and will continue to greatly impact students’ ability to access educational resources. Despite the challenges, the research implies that automated literacy education systems for youth and adults, platforms like Braille Tutor that aim improve educational outcomes for disabled and impaired students, and the ongoing development of Intelligent Tutoring Systems and Pedagogical Agents will only enhance our shared interest in creating equitable environments for students to explore the best of who they can become.

References:

Anshari, M., Alas, Y., Sabtu, N. P. H., & Hamid, M. S. A. (2016). Online Learning: trends, issues and challenges in the Big Data Era. Journal of e-Learning and Knowledge Society, 12(1).

Bawa, P. (2016). Retention in online courses: Exploring issues and solutions—A literature review. Sage Open, 6(1), 2158244015621777.

Bowers, J., & Kumar, P. (2015). Students' perceptions of teaching and social presence: A comparative analysis of face-to-face and online learning environments. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 10(1), 27-44

Breeden, T. (2019, June 10). Work 1: Annotated Bibliography. The Automation of Authentic and Functional Pedagogies. Retrieved from https://cgscholar.com/community/profiles/user-63347-87159/publications/187405

Eguchi, A. (2015). Promoting Innovation Literacy in Education through Educational Robotics. In D. Rutledge & D. Slykhuis (Eds.), Proceedings of SITE 2015--Society for Information Technology & Teacher Education International Conference (pp. 1908-1917). Las Vegas, NV, United States: Association for the Advancement of Computing in Education (AACE). Retrieved May 19, 2019 from https://www.learntechlib.org/primary/p/150262/.

Erdogan, N., Sencer Corlu, M., & Capraro, R. M. (2013). Defining innovation literacy: Do robotics programs help students develop innovation literacy skills? International Online Journal of Educational Sciences, 5(1).

How Access to Technology Can Create Equity in Schools. (2019, April 29). Retrieved June 15, 2019, from https://digitalpromise.org/2019/04/29/equity-in-schools-access-technology/

Jacovina, E. J., & McNamara, D. S. (2017). Intelligent tutoring systems for literacy: Existing technologies and continuing challenges. In R. Atkinson (Ed.), Intelligent tutoring systems: Structure, applications and challenges. Hauppauge, NY: Nova Science Publishers Inc. To be published with acknowledgment of federal support.

Johnson, A. M., Guerrero, T. A., Tighe, E. L., & McNamara, D. S. (2017, June). iSTART-ALL: confronting adult low literacy with intelligent tutoring for reading comprehension. In International Conference on Artificial Intelligence in Education (pp. 125-136). Springer, Cham.

Kandlhofer, M., Steinbauer, G., Hirschmugl-Gaisch, S., & Huber, P. (2016, October). Artificial intelligence and computer science in education: From kindergarten to university. In 2016 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE.

McCarthy, T., Rosenblum, L. P., Johnson, B. G., Dittel, J., & Kearns, D. M. (2016). An artificial intelligence tutor: a supplementary tool for teaching and practicing Braille. Journal of Visual Impairment & Blindness, 110 (5), 309-322.

SIGCHI, A. (2016, April 25). Effects of Pedagogical Agent's Personality and Emotional Feedback Strategy on Chinese Students' ... Retrieved from https://www.youtube.com/watch?v=8ORg54ARTlk.

Techbridgeworld. (2016, June 28). The Braille Tutor at the Mathru School for the Blind. Retrieved from https://www.youtube.com/watch?v=_TXiQWdI308.

WritingPalStudy. (2018, May 10). ISTART Demonstration. Retrieved from https://www.youtube.com/watch?v=aozJMXIMVfM#action=share

Xiang Feng, Xiaoran Guo, Longhui Oiu, & Rui Shi. (2018). Transferring Human Tutor’s Style to Pedagogical Agent: A Possible Way by Leveraging Variety of Artificial Intelligence Achievements. https://doi-org.proxy2.library.illinois.edu/DOI: 10.1109/TALE.2018.8615413.