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The AI Classroom Review

The AI Classroom Review

Table of Contents

Have we ever wondered what it would be like to step into the future of education, guided by an entity that never grows tired or impatient? Our curiosity led us to “The AI Classroom: The Ultimate Guide to Artificial Intelligence in Education Paperback – Big Book, 1 May 2023.” Let’s see what we unearthed.

The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023

Get your own The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023 today.

First Impressions

Our first thought on receiving “The AI Classroom” was the heft of the book. It’s a substantial volume that promises a deep dive into the burgeoning world of AI in education. But is it worth the wrist strain? Matteo-small talk aside-let’s navigate this together.

The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023

The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023

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The Cover: A Glimpse Into the Future

The book cover itself deserves a shout-out for its sleek, modern design which offers a peek into the high-tech yet accessible content within. It’s as if the cover art is whispering, “Yes, AI can be friendly and not just the stuff of science fiction nightmares.” We were ready to be enlightened.

Learn more about the The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023 here.

Authors’ Credibility

Who’s Behind the Magic?

This book is brought to us by a group of seasoned experts in both artificial intelligence and education. Their experience shines through each page, presenting not only a treasure trove of knowledge but also practical insights and applications. We felt like we were in good hands and trust is a big deal when you’re diving into something as complex as AI.

Content Overview

The Chapters: A Layman’s Paradise

We found each chapter to be a well-thought-out journey from the basics of AI to its complex applications in real-world educational settings. The language strikes a perfect balance between informative and conversational, keeping us hooked without feeling overwhelmed.

Practical Applications: Case Studies Galore

The book is peppered with real-life case studies that demonstrate the transformative power of AI in education. These stories give more than just a theoretical backdrop; they offer a glimpse of how AI is actually making a difference today.

The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023

Breaking Down AI Concepts

Understanding AI

In the opening chapters, the authors achieve the impossible—they make sense of AI for those of us who aren’t computer science graduates. By the end, we felt like we could hold a conversation about neural networks without sounding like we’d swallowed a Wikipedia page.

AI and Machine Learning: BFFs or Frenemies?

The book does an excellent job clarifying the difference between AI and machine learning. The use of simple analogies and easy-to-understand language ensures we aren’t left scratching our heads.

Concept Description Example
AI The broader concept of machines being able to carry out tasks in a smart way. Voice assistants like Siri.
Machine Learning A subset of AI that involves machines learning from data and improving over time without explicit programming. Netflix recommendations.

Practical Tools and Tips

AI in Real Classrooms: A Day in the Life

A particularly enjoyable section showcased a hypothetical “day in the life” of a classroom fully integrated with AI tools. By breaking down an average school day, the book gives us a tangible sense of how AI can support learning, from personalized lesson plans to real-time feedback.

Teacher’s Little Helper

We were particularly taken with the chapters on AI tools designed to assist teachers in administrative tasks. The discussion on AI-driven grading systems had us dreaming of a world where educators can focus more on teaching rather than paperwork.

The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023

The Ethical Debate

Should We or Shouldn’t We?

We appreciated that the authors didn’t shy away from discussing the ethical implications of AI in education. Questions about data privacy, the digital divide, and algorithmic bias are tackled head-on. It’s not all sunshine and rainbows, and that honest discourse makes the guide all the more credible.

Balancing Act: Technology vs. Human Touch

While the book is optimistic about the potential of AI, it also emphasizes that technology should complement, not replace, the human touch in education. This balanced perspective ensures we aren’t being sold a one-sided vision of the future.

Real-world Examples

Success Stories

One of the book’s strengths is its plethora of real-world examples demonstrating AI’s successful implementation in educational settings. Schools from diverse geographical locations and socio-economic backgrounds are highlighted, showing the universal applicability of AI.

Not Just for the Privileged

By including examples from various educational systems worldwide, the book underscores that AI isn’t just for the tech-privileged elite. With thoughtful integration, it can serve students and educators from all walks of life.

Resources and Further Reading

Apps and Websites: Your AI Toolkit

The final chapters provide a list of recommended apps, websites, and other resources that we found incredibly useful. It’s like finding a treasure map at the back of a pirate novel—exciting and full of promise.

Workshop Ideas for Educators

For those of us inspired to champion AI in our educational settings, there are workshop ideas and templates. We felt empowered to take the first steps towards bringing the AI classroom to life in our own schools and communities.

FAQs: Clearing the Fog

FAQ Table

And just when we thought the book couldn’t give us more, it ends with a comprehensive FAQ section. It’s like they read our minds—or maybe our search histories.

Question Short Answer
Will AI replace teachers? No, it’s meant to assist and enhance traditional teaching methods.
How secure is student data with AI tools? Most AI systems adhere to stringent data privacy regulations, but it’s essential to choose reputable tools.
Can AI work with any curriculum? Yes, AI tools can be customized to fit various curricula and educational standards.

Final Thoughts

As we closed “The AI Classroom: The Ultimate Guide to Artificial Intelligence in Education Paperback – Big Book, 1 May 2023,” we felt a mix of relief (no more wrist strain) and sadness (saying goodbye to newfound knowledge buddies). In a world flooded with techno-babble and empty promises, this book stands as a beacon of clarity and practicality. If we are even a little curious about the intersection of AI and education, this big book is a definite must-read. With its authoritative voice, accessible language, and practical advice, it’s like having a very knowledgeable, very patient friend guide us into the classroom of the future.

So, what do we say? Shall we give “The AI Classroom” a shot and see just how far down the rabbit hole of AI in education we can go?

Check out the The AI Classroom: The Ultimate Guide to Artificial Intelligence in E Paperback – Big Book, 1 May 2023 here.

Disclosure: As an Amazon Associate, I earn from qualifying purchases.

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Emmanuel Bazile All Rights Reserved

About Me

With 25 years of experience in healthcare IT implementation, Emmanuel began his career at the University of Pittsburgh Medical Center, working as an assistant manager for a billing system implementation. Over the years, he has explored various aspects of the healthcare IT domain, successfully implementing several laboratory information systems and electronic medical record (EMR) systems, such as Cerner Millennium and Epic EMR.

In 2005, Emmanuel shifted his focus to public health, working on bio-surveillance implementation for the Centers for Disease Control and Prevention (CDC). He contributed to the BioSense Data Provisioning Project and performed extensive analysis of HL7 messages in hospitals and healthcare facilities. Additionally, Emmanuel requirements analysis for the CDC BioSense Analysis, Visualization and Reporting (AVR) project and played a key role in publishing the Situational Awareness updates to the BioSense System Requirements Specification (SRS).

Over the past 11 years, Emmanuel has worked in the Middle East, implementing the Epic EMR system at Cleveland Clinic Abu Dhabi. As a multidisciplinary team member, he has taken on various roles, including SCRUM Master, Project Manager, Integration Engineer, and Platform Engineer. Concurrently working as an adjunct university faculty member, teaching graduate-level courses in Systems Life Cycle and undergraduate courses in Health Information Systems

From a technological standpoint, Emmanuel has designed, installed, and implemented complete hospital integration systems using Rhapsody Integration Engine, MS SQL Server, and Public Health Information Networks Messaging System (PHINMS). He has also developed over 10,000 interfaces some of which coded in Java and JavaScript.

In 2019, Emmanuel expanded his skill set and entered the field of digital marketing, quickly becoming a proficient Digital Marketing Strategist. He has since helped numerous clients develop robust digital marketing strategies for their businesses. His expertise encompasses Social Media Marketing, On-page and Off-page SEO, Google Ads, and Google Analytics. Additionally, he and a team have managed clients’ website development projects, ensuring that each site is optimized for SEO, further enhancing their online presence and performance.

Alongside their digital marketing expertise, Emmanuel has delved into the world of Affiliate Marketing, where Emmanuel and his team successfully managed and executed campaigns for a variety of clients. By identifying the right products and services to promote, Emmanuel and his team helped clients generate passive income streams and increase their overall revenue.

Their approach to Affiliate Marketing involves creating valuable content that educates and engages the target audience, while strategically incorporating affiliate links. Emmanuel and his team have experience working with multiple affiliate networks and platforms, ensuring optimal tracking and reporting of performance metrics. By staying up to date with the latest trends and best practices, Emmanuel and his team have been able to optimize affiliate campaigns for maximum results, fostering long-term partnerships and sustainable growth for their clients.

As an accomplished professional, Emmanuel holds dual Bachelor of Arts degrees in Linguistics and English, a Master of Science in Health Information Systems from the University of Pittsburgh, and a Ph.D. in Information Systems from Nova Southeastern University.

My Teaching History

Professor Bazile is a dedicated technology instructor and Adjunct Faculty professor, who began his teaching career in April 2000 at the Business Career Institute in Las Vegas, Nevada.

In 2001, he expanded his expertise by training nurses in the use of Electronic Medical Records (EMR) systems. His experience in both technology and healthcare led to his appointment as an Adjunct Faculty professor at the University of Phoenix in May 2008, where he has taught several graduate-level information technology and healthcare information systems courses.

Dr. Bazile has also developed an HL7 course, which he has taught at various healthcare facilities, drawing from his own book, “HL7: Introductory and Advanced Concepts,” currently available on Amazon. With a passion for teaching and a commitment to ensuring students get the most out of each course he teaches, Dr. Bazile is a valuable asset to both his students and the institutions he serves.

My Teaching Philosophy

My teaching philosophy as an Information Systems professor in healthcare is built on the concept that education should equip students to be confident and capable problem solvers who are prepared to traverse the complicated and ever-changing landscape of Healthcare IT.

In order to accomplish this, I prioritize the creation of a dynamic and engaging learning environment that encourages students to engage with course material and with one another. This involves employing a range of teaching approaches, such as lectures, seminars, and hands-on activities, to ensure that students learn in the manner that best matches their learning style.

I believe the reason we have Information Systems as a discipline is to allow students to apply technology to solve real world problems. If that is the case, both undergraduate and graduate students have to be challenged to incorporate their core academic courses with their matriculated subjects. As such, it is important that students enter their Junior and Senior years with a strong command of the core courses such as Programming, databases, networks, hardware and software, as they serve as the foundation upon which real-world solutions will be built.

I also believe in the importance of incorporating real-world examples and case studies into my courses, as this helps to connect abstract concepts to practical applications. Additionally, I encourage students to apply what they are learning to their own personal and professional goals, as this helps to make the material more meaningful and relevant to their lives.

I strive to foster a positive and supportive learning environment where all students feel comfortable asking questions and participating in class discussions. I believe that this is key to fostering a sense of community and ensuring that all students have the opportunity to succeed.

I have also taught online courses. I have found in an asynchronous learning environment it can be difficult to apply the Peer Teaching or Experiential Learning Pedagogical Approaches. However, I have found the Discovery Learning approach to works quite well. Along with a boost to students’ self-confidence, Discovery Learning in an online environment allows students to synthesize information, expand on existing concepts on their own, while experiencing a positive outcome through trial and error.

Ultimately, my mission as an educator, and a Healthcare IT Information Systems professor is to provide students with the knowledge, skills, and confidence they need to thrive and succeed in their careers and to be technological leaders. By creating a positive and supportive learning environment, incorporating real-world examples and case studies, and encouraging students to apply what they are learning to their own objectives; my hope is to inspire and empower all students to achieve their full potential.

Population Size:

A total of 310 responses were originally received. Any response containing missing data due to unclicked radio buttons or unchecked checkboxes were first reviewed, and, if justified, were omitted from analysis. For surveys with missing data, a total of 18 responses were removed. In order to address any issues with response-set, the data was downloaded into Microsoft Access and queries ran to identify responses that contained the same values for each question. A total of 16 responses were found to be qualified for removal. Another 18 were identified as outliers and removed leaving a total of 258 responses for the study analysis.

In order to assess multivariate outliers, the Mahalanobis distances were calculated and plotted against their corresponding Chi-Square distribution percentiles (Schmidt & Hunter, 2003). The resulting scatterplot is similar to a univariate normal Q-Q plot, where deviations from a straight line show evidence of non-normality. The data showed indications of moderate deviations from multivariate normality, as indicated by the concavity of the data points. There were no additional multivariate outliers or missing values in the data after the removal of 52 responses.

Descriptive Statistics

Frequencies and percentages were conducted for the demographics indicators, while means and standard deviations were calculated for the continuous indicators. For gender, there were 151 females (59%) and 107 males (41%) in the sample. For ethnicity, most participants were Caucasian (119, 46%), followed by African American (56, 22%). The two most populous education levels were Bachelor’s (90, 35%) and Master’s (62, 22%). The biggest proportion of the sample by age group was the 35-44 age group (101, 39%) followed by the 45-54 age group (59, 23%).

Analysis:

Confirmatory Factor Analysis and Composite Reliability

A CFA was conducted along with a reliability analysis to assess construct validity. Examination of modification indices and factor loadings indicated that CSE1, CSE5, CSE7, PC5, ATE1, ATE6, ATE8, PP5, and PP6 were all causing significant problems with the model parameters. The results of the last iteration of the CFA performed in R showed significantly improved fit, although still poor overall (χ2(545) = 2125.61, p < .001, CFI = 0.82, TLI = 0.81, RMSEA = 0.11). The high degrees of freedom indicate that a very large number of parameters are being estimated in this model.

Composite Reliability

For the full model, each construct had excellent reliability. The ATE latent construct had a composite reliability value of 0.89. The ORC construct had a composite reliability value of 0.94. The CSE latent construct had a composite reliability value of 0.85 and PC had a composite reliability value of 0.95. For PP and RES, the composite reliability scores were 0.80 and 0.96 respectively. These values indicate that the loadings for each construct were all directionally similar, and that the items in each construct show a high degree of consistency.

Cronbach’s Alpha

Cronbach’s alpha values were calculated for the items in each construct. The alphas for PC (α = 0.90), AXY (α = 0.94), and RES (α = 0.94) indicated excellent reliability. The alphas for CSE (α = 0.80), ATE (α = 0.88), and PP (α = 0.83) all showed good reliability. These values confirm the results of the composite reliability tests, and reiterate the high degree of reliability within each latent construct.

Partial Least Squares – Structural Equation Modeling

A partial least squares- structural equation modeling (PLS-SEM) was conducted to determine how well the data fit the proposed model, and discern whether significant relationships existed between the independent and dependent constructs. The full model showed AVE values of 0.53 for ATE, 0.69 for AXY, 0.44 for CSE, .72 for PC, .35 for PP, and 0.81 for RES. The high values for AXY, PC, and RES indicate that the amount of variance accounted for in the manifest variables is sufficiently high. The values for ATE, CSE, and PP indicate that some of the variance in the manifest variables is left unexplained.

Structural Model

Once the measurement model had been tested for model specification, the structural model was tested to determine if ATE, AXY, CSE, PC, and PP had a significant effect on RES. A path weighted model was calculated using 10,000 bootstrap samples in R. The results showed a pseudo R-squared value of 0.78. This indicates that approximately 78% of the variance in RES is explainable by the collective effects of CSE, PC, ATE, PP, and AXY.

Further examination of the effects indicated that AXY had a highly significant effect on RES (B = 0.87, p < .001). This indicates that a standard deviation increase in AXY increases the expected value of RES by 0.87 standard deviations. CSE did not have a significant effect on RES (B = 0.02, p = .423). Additionally, CSE (B = 0.02, p = .423), PC (B = 0.05, p = .334), ATE (B = 0.00, p = .983), and PP (B = 0.03, p = .407) did not have significant effects on RES. Table 11 outlines the results of the path estimates.

Correlation Analyses

Both Pearson and Spearman correlations were calculated on the composite scores. The results of the Pearson correlations indicated that CSE was significantly correlated AXY (r = 0.22, p < .001) and RES (r = 0.21, p < .001). The results also indicated that PC was significantly correlated with ATE (r = -0.79, p < .001), AXY (r = 0.18, p < .001), and RES (r = 0.20, p < .001). ATE was significantly correlated with AXY (r = -0.19, p < .001) and RES (r = -0.19, p < .001). AXY was significantly correlated with RES (r = 0.85, p < .001).

ANCOVA Analyses

An analysis of covariance (ANCOVA) was conducted to determine if a significant relationship existed between the AXY, PP, CSE, PC, ATE scores and RES controlling for Gender, Age, Ethnicity, Education, and Specialty. The overall model was found to be significant (F(63,194) = 53.39, p < .001), with an R2 value of .95, indicating that 95% of the variance in RES was explained by the collective effect of the independent variables and covariates.

Since the overall model was found to be significant, the model’s covariates were assessed. The AXY (F(10,194) = 262.20, p < .001), ATE (F(7,194) = 2.20, p = .036), Years computers (F(1,194) = 5.71, p = .018), and PC (F(12,194) = 2.00, p = .026) scores were found to be significant, indicating that a significant amount of variance in RES is explained by AXY, ATE, and PC.

A path diagram depicting the results of the structural model.

Results

This research investigated Computer Self-Efficacy (CSE), Perceived Complexity (PC), Attitudes toward EMR Systems (ATE), Peer Pressure (PP), and Anxiety (AXY) to determine whether these constructs as individuals, or as a group, or coupled together with some other factors could significantly explain resistance to EMR systems. Quantitative examination of self-reported survey results was performed to understand the strength and significance of the relationships, while these relationships were investigated to test the strength of model fit.

the regression paths of the structural model were examined to test the hypotheses. Significance was determined using an alpha level of .05. The model had an overall R2 value of 0.78. This indicates that approximately 78% of the variability in RES can be accounted for by CSE, PC, ATE, PP, and AXY. Since the overall model was significant, the individual coefficients can be interpreted. Some of the hypotheses were supported by the results of this study, and some were rejected. The construction of a data model of the relationships in this study could not meet thresholds that would be evidence of a good fit of the relationships identified in the study.

The fifth hypotheses tested the influence of AXY on resistance to EMR systems. AXY was expressed to be significantly related to resistance (r=.87, p<.001). This finding supports the hypothesis that anxiety with the EMR system will lead to medical care professionals rejecting use of the system. Unlike the findings of the first four hypotheses, the findings of the current study support previous research. Angst and Agarwal (2009) indicated that AXY is a factor which is significantly related to the problem of EMR system resistance. Based on the empirical findings of previous research, the present research and conceptual propositions and conclusions in previously written scholarly articles, there is a great deal of support for the finding that AXY is significantly influenced by EMR resistance.

The findings of this research do not support all findings by previous researchers, and there are multiple relationships which had been established as being significant that were identified as being insignificant in the current research. Generally, because of the inconsistency of previous findings and the current study there may be elements related to the sample examined or other contextual factors which may contribute to the inconsistency that exists. Ultimately, it is suggested that there be further research done on the problem of resistance to EMR system use.

Ultimately the findings support a new take on the problem of EMR system resistance that may contribute to the ways in which scholars investigate the problem of EMR resistance in general. This may also help with the way practitioners approach EMR systems, and articulate value of the systems to medical professionals investing record-keeping systems in the workplace.