**Abstract**

The Course-Building technique called PLErify was developed by the author in response to the emerging roles of university faculty in the technology-driven teaching with the rising popularity of AI and deep learning. Topics that support personalized teaching and learning using technology to make it more efficient, more effective and more pragmatic. Early attempts at pedagogy and trends that pushed the personalization movement are explained. The progress of the project in aWeb App format is detailed focusing on a faculty building a sample hybrid course planned for a course offering of a framework of digital resources within the app in a technology-rich smart classroom. The PLErify course-building Template is explained with methodologies to add content to it in various ways with suggestions to insert multimodal techniques, e.g., Augmented Reality, Virtual Reality and Simulation, however applicable, alongside numerical data-science-supported technologies that will comprise the most part of course presentation technique. A portion of a full course will be demonstrated using PLErify with an accompanying Course Evaluation for Professors to mull to prepare for course redesign current to improve next year's offering of same course.

**Keywords:** web application, digital resources, personal learning environment, PLErify, course development, MOOC, didactic, AI

#### **1. Introduction**

In a digital society, every aspect of our daily lives is interconnected and each person has an identity that is solely one's own that is encrypted and authenticable by a system. That identity allows you to interactively access multiple parts of any platform to perform actions and obtain something as a result. In an ideal version of a digital society, we humans are interconnected as citizens (*e-government*) and members of various groups (private) and afforded rights and privileges accordingly. We see micro versions of the workings of a digital society in Big Tech such as Facebook, LinkedIn, Twitter, Instagram, and Google with each connectivity model functioning according to predetermined business model.

Our ever-evolving digital society intertwines the roles of humans and robots in institutions and industries. These roles keep changing as technology advances to near capability of humans through artificial intelligence and deep learning permeating the deepest trenches of every industry, not excluding higher education. In higher education, it is hardly noticeable that the roles of main players (*faculty and instructors*) as primary owners, designers, and deliverers of their own courses for live instruction need to step up and adapt more aggressively more so than any other group. Not to be

confused with purely online learning, tech-driven courses go beyond use of learning management system, LMS. PLErify use of private server, resource, and tool-based application is one such solution and discussed below.

## **2. Emerging faculty role in an AI-driven scenario.**

*"If we teach today's students as we taught yesterday's, we rob them of tomorrow". John Dewey*

An American philosopher and educator, John Dewey (1859–1952) gave a very powerful quote with a whole new meaning that is truer now than in his time. Truer now because the educational methods we now deal with goes beyond the chalkboard, goes beyond talking in front of students, and goes beyond doing projects in isolation using pen and paper. Not completely discounting the power and value of note-taking using pen and paper and would not advise against the method, it is important to recognize the presence of computational tools being used as part of current teaching methods he would have never imagined would exist today.

Undeniably, educational technology tools ushered the transition from passive (*sit, listen, take notes*) to active (*interactive "constructivist" learning*) with students defining their knowledge accumulation, construction, and learning pathways. Deep learning, Machine Learning, Big Data and Internet of Things (*IoT*), and artificial intelligence disrupting education in more pervasive ways have no specific timeframe. While innovation and adaptation slowly chip away traditional education system, the idea of faculty being put aside with little to no role in designing, offering, syndicating, and delivering courses (*aptly described as course massification*) is in fact a repelling thought. In universities however, few scenarios must play out to avoid a scenario where the machine decides and controls. The professor must play the central role but in an enhanced strategic [1] and impactful way. They (*professors*) must assume leadership roles to formulate actionable changes but be cognizant and fully prepared to face added responsibilities from programming robot consciousness to designing robotized courses through aggregating and updating content, that is, build virtual (*academic versions of Alexa-like*) robot assistants [2], automated pulling of content from a variety of resources. These new robot-driven challenges in higher education are succinctly described as follows.

#### **2.1 Robots and professors for efficient teaching**

Widely practiced in Japan, Korea, Taiwan, Singapore, and China, are robots (built in the likeness of a professor/researcher), robot applications, and robots programmed to co-teach/co-research juggling the myriad roles of the human instructor. Other foreseen creative uses of these robots involve individualizing attention to each student, thereby ensuring progress, remediation, and success (knowledgebase-driven virtual assistants). Missing in those possible roles are robots that build online courses for professors based on didactic teaching styles and student learning styles all utilizing high integrity knowledge bases with optimum performance. Past attempts at course development using course sequencing [3], adaptive learning paths [4], computational teaching, and participatory teaching [5] can inspire new innovations in this area. One deep-learn course building technique is an AI-based course aggregator (*software-based*) which pulls different curriculum (using Big Data) of the same course from many places/universities and then gets stored to a central location where students get to pick and choose a course program of study. These new courses would be up-to-date with new data that include recent

**51**

*Personalizing Course Design, Build and Delivery Using PLErify*

*behaviorist, and mental models*) has been intentionally skipped.

bypassed having mastered it beforehand through MOOC.

While debates and experimentation continue to grow in artificial intelligence, PLErify App (2007) remains a precursor to the above scenarios. Even though the core of educational technology research centers on academic applications, academe, ironically remains the most resistant and the slowest to adapt to a scenario of AI, Big Data and IoT whichwhen taken as a group suddenly changes the course delivery game. Groups in the tech industry persistently hint at a future without a human teacher and professor, as computer scientists now and then flirt with the idea of adding consciousness to a computer. At this time though, a robot cannot actually augment human cognitive and emotional capabilities through what they claim as smarter machines currently experimented in other industries (automobile industry). I would simply and safely assume that use of virtual robot assistant is an easy spillover for use in higher education [2].

developments in the discipline. Since my focus on PLErify is to assist the instructor, the adaptive learning concept for learners based on any learner model (*cognitive,* 

**2.2 The MOOC-as-course augmentation for faculty and as resource for PLErify** 

In AI age, faculty must face their new roles as programmer and owner/builder of learning environments of their courses that they must update per semester. Professors who remain indifferent in the new reality of a virtualized higher education vis-a-vis their expanded roles and new responsibilities will face major challenges as industry-driven automation-driven AI persistently seek dormant or stagnant unchanging areas to automate and simplify. A faculty [6] from Scotland recalls his very productive sabbatical spent at Google (*Big Tech and AI-driven*) in Silicon Valley. In that sabbatical, this academically trained faculty learned and eventually re-adapted his purely academic mindset (coding) to meld with that of a practitioner's mindset and started building coding projects that work in real life. Faculty (*particularly those in the sciences and engineering*) can follow Barker's example and come out technologically empowered fusing the academic with the practical real world and impart the same mentality to its students, that is, college to career. Regardless, MOOC courses will continue to be made available online to anybody for free, or at a minimal cost. Boosting acceleration of acceptance by universities globally, MOOC continues an upward evolution toward a better practical higher education option for both career (*skills training or mastery certifications system*) and advanced degrees (*bachelors, masters, and doctorate*)*.* Also, with student advancement (*and their interest in mind*) and ease of teaching for instructors as prime motivators for MOOC development, there is truly so much about the MOOC system to be appreciated that will make higher education leaner, more efficient, very current, and very affordable. Another very encouragingly useful aspect of MOOC that is only now being realized is, they add to the personalization of learning both as a teaching resource for instructors and as an inexpensive way for students to obtain advanced degrees and lastly but more importantly to upgrade skills of work professionals. MOOC business model is being revamped and, evolving toward a more profitable version, thereby offering fee-based enrollments where certification of completion is a student's objective. The free aspect of the MOOC model however can be used by all faculty as another teaching tool. For example, professors can require students to take the MOOC version of their course offered by other universities (*now estimated at 900*) shown in **Figure 1**, before students take the real course. In a PLErify state, courses already in MOOC database can be treated as a required mastery before registering and enrolling in the equivalent actual university traditional course or program unloading faculty of heavy teaching. Some tedious portions of the course can be

*DOI: http://dx.doi.org/10.5772/intechopen.85414*

**teaching learning**

*New Innovations in Engineering Education and Naval Engineering*

application is one such solution and discussed below.

higher education are succinctly described as follows.

**2.1 Robots and professors for efficient teaching**

**2. Emerging faculty role in an AI-driven scenario.**

confused with purely online learning, tech-driven courses go beyond use of learning management system, LMS. PLErify use of private server, resource, and tool-based

*"If we teach today's students as we taught yesterday's, we rob them of tomorrow".*

An American philosopher and educator, John Dewey (1859–1952) gave a very powerful quote with a whole new meaning that is truer now than in his time. Truer now because the educational methods we now deal with goes beyond the chalkboard, goes beyond talking in front of students, and goes beyond doing projects in isolation using pen and paper. Not completely discounting the power and value of note-taking using pen and paper and would not advise against the method, it is important to recognize the presence of computational tools being used as part of current teaching methods he would have never imagined would exist today.

Undeniably, educational technology tools ushered the transition from passive (*sit, listen, take notes*) to active (*interactive "constructivist" learning*) with students defining their knowledge accumulation, construction, and learning pathways. Deep learning, Machine Learning, Big Data and Internet of Things (*IoT*), and artificial intelligence disrupting education in more pervasive ways have no specific timeframe. While innovation and adaptation slowly chip away traditional education system, the idea of faculty being put aside with little to no role in designing, offering, syndicating, and delivering courses (*aptly described as course massification*) is in fact a repelling thought. In universities however, few scenarios must play out to avoid a scenario where the machine decides and controls. The professor must play the central role but in an enhanced strategic [1] and impactful way. They (*professors*) must assume leadership roles to formulate actionable changes but be cognizant and fully prepared to face added responsibilities from programming robot consciousness to designing robotized courses through aggregating and updating content, that is, build virtual (*academic versions of Alexa-like*) robot assistants [2], automated pulling of content from a variety of resources. These new robot-driven challenges in

Widely practiced in Japan, Korea, Taiwan, Singapore, and China, are robots (built in the likeness of a professor/researcher), robot applications, and robots programmed to co-teach/co-research juggling the myriad roles of the human instructor. Other foreseen creative uses of these robots involve individualizing attention to each student, thereby ensuring progress, remediation, and success (knowledgebase-driven virtual assistants). Missing in those possible roles are robots that build online courses for professors based on didactic teaching styles and student learning styles all utilizing high integrity knowledge bases with optimum performance. Past attempts at course development using course sequencing [3], adaptive learning paths [4], computational teaching, and participatory teaching [5] can inspire new innovations in this area. One deep-learn course building technique is an AI-based course aggregator (*software-based*) which pulls different curriculum (using Big Data) of the same course from many places/universities and then gets stored to a central location where students get to pick and choose a course program of study. These new courses would be up-to-date with new data that include recent

*John Dewey*

**50**

developments in the discipline. Since my focus on PLErify is to assist the instructor, the adaptive learning concept for learners based on any learner model (*cognitive, behaviorist, and mental models*) has been intentionally skipped.
