Category: 6030

Prompt: In your educational domain (e.g., K-12, higher ed, corporate), what type of XR would be useful to enhance your instructional activity? Why?

I am lucky to work at an innovative institution where XR technologies are used to enhance student learning. I recently changed positions within my department to lead the 3D innovations team, and I am excited to pair my educational studies with the good work that’s being done in my department.

My colleague, Gary Wisser, is a very talented 3D developer. Our medical professors will bring him bones to scan, which he then converts into digital 3D renderings. These digital assets are uploaded to Sketchfab for other medical schools to use. He’s been able to help professors enhance student learning with his 3D rendering because the assets are no longer 2D images. Students can move the view and manipulate the bones to see how the body moves. This helps in their learning and understanding of the body. I have included a few examples of Gary’s work below.

Another project we currently have underway is a virtual surgery room for our college of podiatry. Here students can practice their skills in a safe environment. This is an ongoing project that was developed for students to experience in a full immersion VR headset; however, a web-based version is also available for those without a headset. This project will be complete and ready for student use by the end of 2022, and then we will be moving on to Phase 3 of this project and adding haptic feedback to the surgery simulation.

Medical education is a great platform for mixed reality education. Patient simulations in VR and AR are just the beginning. I am in talks with other professors about how we can bring medicine to the Metaverse, and what that might look like for our future students. Haptics is another great addition to the XR world, and we are testing out the impact those devices will have on medical procedures.

It’s an exciting time to be in the medical education field!

Resources:

Moore, T. (2020, November 12). Projects & Innovations. CETL. Retrieved October 23, 2022, from https://cetl.westernu.edu/cetl-projects-innovations/

Wisser, G. (2022). WesternU3D. Sketchfab. Retrieved October 23, 2022, from https://sketchfab.com/WesternU3D

Prompt:

What is the role of the federal government and/or big tech companies is policing deepfakes and shallowfakes?

Response:

Ironically, the system used by the deepfakes industry to create and distribute false imagery is the same one in which some services including Facebook and Twitter are investigating how to remove them. It is by drawing out in more detail the connections between deepfake technology and the platforms in which it’s being distributed that we may have more success in understanding the scope of the problem and targeting the enforcement of laws around it.

The services that hosted deepfakes images and videos in 2017 identified them as violating their terms of service; Facebook has a team of people who take down content that violates its policies; and YouTube even created a team devoted to handling this problem in April 2017. The number of deepfakes-related incidents on platforms has been growing quickly. Twitter has suspended about 30 accounts for violating its terms of service related to the production and distribution of deepfake videos. But it’s hard to see how we can effectively police these platforms in order to deal with the emerging problem of deepfakes — unless lawmakers step in.

Facebook’s focus on cracking down on bad content, especially image and video, from networks affiliated with foreign governments and elections, could be related to how Facebook’s algorithm shapes what you see in your News Feed. A good case study is the rise of “false news” and propaganda circulating on the platform following the 2016 presidential election. As a result, Facebook instituted a series of news integrity initiatives and tools. But for whatever reason, the company has not responded to the emergence of deepfakes and the ways they are used to sow disinformation.

Could Facebook and other social media platforms be setting a dangerous precedent by allowing so-called “truthiness” to flourish without considering what consequences this might have?

The key to understanding the deeper meanings behind deepfakes, is that they highlight two areas of concern: the use of AI technology to create artificial imagery; and the real-world consequences these images could have. In other words, it’s not the technology that’s dangerous. It’s the effect it could have.

Consider the difference between a faked video and a fake photograph. Facebook may have responded to the use of its AI technology to create fake video images by setting up teams to respond to false video content, but what about all the fake images of a staged assassination and child pornography and videos of animal abuse that are also hosted on Facebook? What about the inappropriate text and video content that were allowed to stay online, after Facebook received multiple complaints?

In other words, what does it say about the algorithms Facebook uses to manage user content that it’s not even willing to try to manage fake video content? Facebook has algorithms that can be used to detect and flag posts that violate its rules, but they don’t yet exist for detecting fake videos, let alone high-quality deepfakes.

The platforms that host deepfakes did not respond to multiple requests for comment on this issue. The platforms did, however, point to the many internal tools they have used to stop the spread of their services in the past. For example, Facebook, YouTube, and Twitter have all used features in their core services, such as spam detection and counter-offensive against troll accounts, to limit the spread of fake content and counter disinformation efforts. But the deepfakes challenge is different: we’re not just dealing with spammers, but with real, high-quality, fake content that affects the political and social discourse online.

It’s hard to understand why Facebook, YouTube, and Twitter haven’t seen the deepfakes challenge coming, and haven’t figured out how to deal with the fact that the services they’ve built have been turned into new ways for people to create, share, and distribute authentic, fabricated, and fake content. If the content were truly fabricated and/or fabricated content, as opposed to the real-world consequences, they would be seeing a surge of comments and messages on their platforms expressing their concerns. But the platforms’ algorithms and employees are trained to quickly ignore the message.

We can’t always control the creation of fake content. But we can take steps to address the real-world consequences, while ensuring that platforms continue to innovate in how they’re used to convey information.

Real Response:

If you made it this far in reading my post you will discover that the text above was actually written by an AI. (SHOCKING!) In my research on this topic, I discovered a free web-based AI tool that will write your response prompt for you.

I simply put in the initial instructor question/discussion board topic, and the free AI online generator wrote the rest. The AI generator gave me the option of how many words I wanted the text to be, as well as the option to add in specific keywords to be included in the generated text. At first glance, the only issue I identified with the AI-generated response was that there were no sources for the comments or figures mentioned. Additional advanced options for this AI-generated text could include the level of creativity in the response, as well as a probability threshold for the number of sampled sources.

The generated text was quite well written, so my second thought was to run it through a free plagiarism detector. I am astonished to report that this AI-generated text was 100% plagiarism-free. It is a completely original work, created by an AI deepfake.

Lastly, as a standard practice of my submission process, while typing this post I have a grammar and spelling bot checking my work. The AI-generated work returned with no spelling issues, and minimal grammar flags; with the only grammar flags being three commas that the grammar bot suggested be removed.

Going back to the original discussion board question, What is the role of the federal government and/or big tech companies is policing deepfakes and shallowfakes? I think the better question is, what can they do to identify deepfakes? The AI-generated response is well written but not perfect, allowing for the potential of human error. The AI-generated original content, so how would the government or big tech company even be able to identify the falsehood? Based on the deepfake content above, what would even cause one to flag it for concern? This topic is a large can of worms, and right now there is no way to put the worms back in the can.

Prompt: Why is it so difficult to bring gamification to K-12 when it appears to be happening in the corporate training world?

When considering the prompt, the initial question instead should be: to what degree should gamification be integrated into K-12 education? One could argue that gamification has already been implemented within the K-12 environment for decades. From a kindergartener selecting a toy from the treasure chest as a reward for good behavior to a high schooler getting a high score in the pre-test Kahoot! review, gamification already has had an impact on student learning.

Students receive awards for successful marks in their academics; these usually come in the form of a letter grade. “Our current grading system encourages competition among and between students. When you consider how grades determine class rank, access to certain classes, opportunities for extracurricular activities, and the subsequent stratification results, how much are they truly representative of knowledge or understanding?” (Shelton, 2021). Students are rewarded with the corresponding grade dependent on how well they play by the rules of the game. Was the work turned in on time? Did the work meet all the rubric requirements? Were the test scores measured by the student’s ability to memorize content? School is a game, and if you play the game well then you Level-Up to higher ed where the game continues.

Should gamified theories and course design have a stronger hold in K-12 education? Perhaps. After all, gamification is rooted in the theory of behaviorism and the foundational conditioning of animalistic instincts (Ju, 2020). The positive enforcement is the grade for a job well done, and the negative reinforcement is the poor grade for poor marks.

Yet, what if a larger gamified-structured curriculum was developed to reward/remediate students? This gamified learning system would be reminiscent of the previous discussions on personalized learning paths and the integration of artificial intelligence technologies. Students could follow their personalized learning path, which includes gamified design principles, such as: “goals and challenges, personalization, rapid feedback, visible feedback, freedom of choice, freedom to fail, and social engagement” (Wang, 2022).

Will (2022) argues that now is the time to implement systematic change within schools. The pandemic forced educators to rethink traditional education and implement new ways of learning. One positive outcome of the pandemic teaching was that it forced educators to put an emphasis on student-centered learning, rather than on testing requirements and meeting benchmarks (Will, 2022). The months ahead will determine the overall impact of the pandemic on student education. Will educators go back to their old, archaic ways? Or do we take this once-in-a-lifetime opportunity to reinvent education that is more student-focused, challenging, personalized, and full of choice? Will this be the springboard to gamify learning?

Resources

Ju, H. (2020). Gamification education – behaviorist theory. Gamification Education – Behaviorist Theory. Retrieved September 26, 2022, from https://www.gamification.education/learning-theories/behaviorist-theory

Shelton, K. (2021, June 9). Grading is capitalist, racist, and exploitative. Medium. Retrieved September 26, 2022, from https://medium.com/age-of-awareness/grading-is-capitalist-racist-and-exploitative-423c0c04742c

Wang, Y.-F., Hsu, Y.-F., & Fang, K. (2022). The key elements of gamification in corporate training – The delphi method. Entertainment Computing, 40, 100463. https://doi.org/10.1016/j.entcom.2021.100463

Will, M. (2022, September 15). Teachers are ready for systemic change. are schools? Education Week. Retrieved September 26, 2022, from https://www.edweek.org/leadership/teachers-are-ready-for-systemic-change-are-schools/2022/09

In your educational domain, for example, K-12, higher ed, or corporate, what type of learning analytic data would be useful to enhance your instructional activity? Why?

In the domain of employee professional development, learning analytics is not currently utilized at my higher education institution. We conduct employee training opportunities in the form of live webinars, archived recorded sessions, and just-in-time training modules, and also offer training through paid providers such as LinkedIn Learning and Magna Commons. Employee tracking is only captured if they attend a live webinar, or if they partake in a LinkedIn Learning Course, but these two tracking systems do not speak to one another. Employee education tracking is siloed and not shared between program offerings, and therefore administration does not have a robust snapshot of what the employee is learning.

If learning analytics could be captured within the various learning platforms and then compiled into a database, we could get a clear snapshot of the employee’s learning interests and deficiencies, which would allow us to create new and relevant topics for the live webinar training offerings. Currently, live webinar topics are discussed through a subcommittee that considers recent news article topics or questions employees had over the last month. This subcommittee throws out ideas, at random, to guess which topics might be of interest to the employees. Yet, with live webinar registrations currently yielding an average of fewer than 10 participants of the more than 1,100 employees, it begs the question: Can we do better? With learning analytics, combined across platforms, and synced into a database system, can future professional development program events yield a higher attendance than 1%?

If user interest, and learner reporting, were captured within the various learning platforms provided by the institution, and then those reports were compiled into a combined database, the administration could pull relevant analytical data. That analytical feedback could help drive future programming by connecting to the employee’s interests and strengthing their learning efforts with relevant content.

Resources:

Dashboard Analytics Available for LinkedIn Learning Admins. Dashboard Analytics Available for LinkedIn Learning Admins | Learning Help. (n.d.). Retrieved September 18, 2022, from https://www.linkedin.com/help/learning/answer/a598935

Educational Partners International. (2022). Learning Analytics for Growth – Ask an Educator. YouTube. Retrieved September 18, 2022, from https://www.youtube.com/watch?v=68bKAAWQm1E.

How do machine learning techniques make the “new personalized learning ” possible?   

Machine learning technologies can support higher education students by designing a personalized learning path that is unique to each student, and caters to their interests and strengths. Current challenges in higher education institutions include “disengaged students, high dropout rates, and the ineffectiveness of a traditional “one-size-fits-all” approach to education” (Rouhiainen, 2019). Personalized learning paths, designed through artificial or machine learning technologies, have the opportunity to support student learning by providing just-in-time remedial learning opportunities when a student is struggling with a concept. Additionally, these technologies can deepen the level of student learning when the student shows competency in a particular subject.

Personalized learning has the opportunity to engage students and expand upon their interests. The traditional method of teaching to the middle can be updated so that each student receives the type of education they need, as an individual, to be successful. Personalized learning would provide students with the immediate feedback they need to support their learning process through the use of machine learning technologies (Khurana, 2018). Students could leave school excited to learn, with a focused mindset on continuing education that expands upon the knowledge they learned in the classroom.

Should personalized learning be applied to all topics in the K-20 curriculum? Why or why not??

We’re not ready yet. The impact of the COVID-19 pandemic, and the quick pivot to emergency remote instruction thrust society into the realm of online learning sooner than expected. Over the two years of the pandemic teaching and learning was turned on it’s ass, and we uncovered the good and the bad, and now we need to reflect and think about where to go from here.

Pertaining to personalized learning, how do we measure that the students are receiving an equal education? Among many things, the pandemic also brought to the forefront the impact of equity in student learning. Some students were without devices, some without internet, some within a roof over their heads, etc. How can personalized learning also be equitable when each student is receiving a unique learning path?

In the case of higher education, if two students are pursuing the same degree but one student struggles with the content and receives hours of remediation content, and the second student is successful in their studies and receives the standard amount of content, or has the ability to dive deeper into the content; how is this equal? In the end, is it fair that both students receive the same degree when they had a different experiences?

Additionally, how would the student competencies be monitored? Western Governors University was under fire for its mode of competency-based education, which has similar attributes to the personalized learning paths. When it comes to accreditation and Title IV (financial aid), it was determined that Western Governors University did not meet the inspector general’s requirements and was facing a potential fine of close to $1 billion (Fain, 2017). This is a larger topic than the prompt for this post, but how will personalized learning paths be fair when competency-based education failed?

Finally, don’t get me started on FERPA. Data leaks are per the norm in today’s society, and adding to the database of student data collection may not be wise at the moment. What if a potential employer discovered that their future employee required remediation? What if that discovery lost that individual the job?

Nope. We’re not ready.

Resources

Fain, P. (2017, September 22). Education dept.’s inspector general calls for Western governors to repay $713 million in Federal Aid. Education Dept.’s inspector general calls for Western Governors to repay $713 million in federal aid. Retrieved September 18, 2022, from https://www.insidehighered.com/news/2017/09/22/education-depts-inspector-general-calls-western-governors-repay-713-million-federal

Khurana, S. (2018, February 6). Personalized learning through artificial intelligence. Medium. Retrieved September 18, 2022, from https://medium.com/swlh/personalized-learning-through-artificial-intelligence-b01051d07494

Rouhiainen, L. (2019, October 14). How AI and data could personalize higher education. Harvard Business Review. Retrieved September 18, 2022, from https://hbr.org/2019/10/how-ai-and-data-could-personalize-higher-education

Prompt: The major weakness of AI/ML systems is that they can’t explain “why”.  That is, an AI/ML system can perhaps prescribe a medication for a cancer treatment, but the system can’t provide an analysis of why it made that decision.  Humans typically wouldn’t trust a physician who couldn’t explain the basis for a prescription decision.  Why can’t AI/ML explain itself.  In your opinion, is that a real problem?

The justification for how or why an AI responds is decided within the programming. If the creator wanted the AI to report how it come to a decision, then the code just needs to be written in a way so that the support for the findings is reported. I disagree with the prompt in that the AL/ML systems can, in fact, explain why but humans would rather not bother with the reasoning behind the answer.

When I attended the annual AMEE (Association for Medical Educators of Europe) conference over the summer in Lyon, France, AI and ML was one of the big topics. Perhaps one of the sessions that I still contemplate was a session titled “Artificial Intelligence: What medical educators should be doing now” (AMEE, 2022). This panel discussion brought together experts from the American Medical Association, the Association of American Medical Colleges, and several university leaders whose research has focused on artificial intelligence and machine learning. My largest takeaway from this session was the discussion about the accuracy of the content filling the AI database.

In medical education, new knowledge is being added daily. It is not uncommon for old treatments to be reversed or proven ineffective. In the 1900’s radium was a medical treatment thought to reduce tumors. Patients would soak in radiation hot springs, and breath in radium-rich gas (Waxman, 2017). While this treatment may seem astonishing today, Waxman (2017) explains that this arcane remedy would eventually lead doctors to discover more appropriate doses for the chemical to treat cancer patients. Now, what if the future AI medical database had included this original radium research, as well as the current research? Which treatment would the AI recommend for the patient if both treatments were equal in the mind of the AI? How would the AI be able to ascertain which treatment remedy is current and correct?

The AI is only as good as the content that is being entered into the system. The message from the AMEE session panel was that doctors should use the database content from the AI database; however, they should also take caution and know enough about medicine to question the AI-suggested treatment results (AMEE, 2022). This got me thinking about AI in general terms, because the same message can be said for any profession. We cannot blindly trust the AI because the AI was created by humans, and humans can be dishonest and make mistakes. We should use the AI systems, but use them with caution because the AI is only as good as the creator and the content that fills the database.

Resources

AMEE. (2022). AMEE conferences. AMEE Lyon 2022. Retrieved October 1, 2022, from https://amee.org/conferences/amee-2022

Waxman, O. B. (2017, October 17). Real historical medical treatments that are terrible for you. Time. Retrieved October 1, 2022, from https://time.com/4982099/quackery-medicine-history/

Now that you’ve seen examples of how digital transformation happens, explain why you believe education hasn’t been digitally transformed like other organizations.  What will it take to truly digitally transform K-20 education? 

Truth be told, there is no incentive for teachers to support digital transformation efforts within educational institutions. The administration has invested in technology to enhance student learning, but faculty have been reluctant to embrace and utilize the technology they have been gifted with. There are many reasons that faculty have been reluctant to change; however, knowing what we know now and all the advances that have been made, we cannot revert to the pre-pandemic way of teaching our students.

Practicing teachers were not taught how to teach using technology, and perhaps their reluctance is due to their technology ignorance. Teachers feel that they lack technical competence in implementing and maintaining new technology in the classroom (Tallvid, 2014). Additionally, Tallvid (2014) noted that teachers felt incompetent in troubleshooting technical issues when it appeared that their students were more tech-savvy than the teacher. Teachers of yesteryear find technology to be unnecessary. Lomba-Portela, et al. (2022) identified that teachers over 50 years of age are more reluctant to change and embrace new technology.

Yet, Ghory & Ghafory (2021), recognized the benefits of integrating modern technology into the teaching and learning process. Research showed that technology increased the students’ motivation to learn, and taught students digital skills that they may apply in the workforce in their future careers.

For education to truly embrace digital transformation, one must start with teacher education. Teacher education curricula must evolve to teach future educators how to impactfully use technology in and out of the classroom. Teachers much feel comfortable using the technology, as well as troubleshooting issues should they arise. Presently employed teachers should receive ongoing, professional development technology training to stay on top of new EdTech trends and understand how technology will benefit students of the future.

References:

Ghory, S., & Ghafory, H. (2021). The impact of modern technology in the teaching and learning process. International Journal of Innovative Research and Scientific Studies, 4(3), 168–173. https://doi.org/10.53894/ijirss.v4i3.73

Lomba-Portela, L., Domínguez-Lloria, S., & Pino-Juste, M. R. (2022). Resistances to educational change: Teachers’ perceptions. Education Sciences, 12(5), 359. https://doi.org/10.3390/educsci12050359

Tallvid, M. (2014). Understanding teachers’ reluctance to the pedagogical use of ICT in the 1:1 classroom. Education and Information Technologies, 21(3), 503–519. https://doi.org/10.1007/s10639-014-9335-7

What was different about DeepMind’s AlphaGo program beating the human Go champ vs IBM’s Watson beating the Jeopardy champs?

Deepmind’s AlphaGo utilized machine learning, and during the Go match that was played against a human player, AlphaGo became increasingly stronger in its ability to make decisions over the course of the match. Whereas IBM Watson utilizes a series of mapped recall question and answer statements; no additional learning took place by the computing system.

Besides the obvious uses of AI/ML in security processes, financial processes, in health processes, etc., where do you see AI/ML’s pattern analysis capabilities really making a contribution? More simply: when will a AI/ML-powered robot do your job? Do the jobs your parents did? Do the jobs your children are being prepared to do?

In my current institution, our department is beginning to dabble with project innovations utilizing artificial intelligence technologies. Our team is creating an empathy chatbot to teach future health professionals how to communicate empathically with their patients. This technology utilizes IBM Watson and employs a string of question and answer statements. It does not utilize machine learning techniques.

In my day-to-day life, however, I find the most recent implementation of AI at the drive-thru menu speaker to be quite fascinating. Our local Panda Express drive-thru has an AI that takes your order through the speaker. At times this can be rather frustrating because the AI needs to go through a series of Q & A selections when taking your order. It takes longer to order through this AI process because the system cannot keep up with the order. Also, if you forget to add something or want to make an edit to a previously ordered item, the system cannot edit these corrections. This implementation of AI within food service is still in its infancy, and it’s only a few years away from being implemented across all fast food service locations, thereby replacing the people who were once originally employed to do this job. Yet, as an aside, with our California minimum wage jumping to $15/hour, it was only a matter of time before the companies sought to replace human employment with more cost-effective technology options. The implementation of AI contributes to cost savings for not just the restaurant but also allows the corporations to provide lower prices to the customer due to the overhead cost savings.

We must consider the jobs of the future when training our future workers; namely our children. My daughter is taking computer coding classes in high school, and perhaps one day she will invent the tech of the future. My son, he’s still a little young and hasn’t decided on his career goals. But, as a tech-savvy parent, I understand the value of STEM education and, at minimum, understanding the technology around us. We must prepare our workers of the future to work with the tech, design the tech, and use the tech because if they are not tech savvy there may not be a successful future for them.

References:

Koenig, R. (2021, March 5). May I take your order? how AI is Changing Fast-Food Drive-Thrus. TODAY.com. Retrieved September 11, 2022, from https://www.today.com/food/may-i-take-your-order-future-ai-fast-food-drive-t210791

Lucas, A. (2022, August 29). Panera Bread tests artificial intelligence technology in drive-thru lanes. CNBC. Retrieved September 11, 2022, from https://www.cnbc.com/2022/08/29/panera-bread-tests-artificial-intelligence-technology-in-drive-thru-lanes.html