10 Myths About examples of artificial intelligence in education

Exploring the impact of artificial intelligence on teaching and learning in higher education

This paper explores the phenomena of the emergence of the use of artificial intelligence in teaching and learning in examples of artificial intelligence in education. It investigates educational implications of emerging technologies on the way students learn and how institutions teach and evolve.

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From AI to Straight A’s: Artificial Intelligence Within Education

I believe that the system could be bolstered by recent advancements in artificial intelligence technology, such as automation and adaptive learning, including gamification and knowledge monitoring.

Artificial Intelligence examples of artificial intelligence in education

When I think back on my own educational experience, I can distinctly remember the impact of technology as the years passed.

By 5th grade, we had all survived Y2K, and the digital revolution had officially begun. Our class had a small set of Alphasmart 2000 keyboard devices so we could begin learning how to type. In 6th grade, our classroom was the site of the school’s first SMART Board, and there was a bulky desktop computer for every 2-3 students.

, but eventually class notes, presentations, modules, and assignments moved online as well.

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Machine learning for human learners: opportunities, issues, tensions and threats

Harm caused by algorithmic activity can be hard to detect and find its cause. Creators of machine learning systems/models should be held accountable for any issues of bias and transparency.

Artificial Intelligence examples of artificial intelligence in education

As we discuss later machine learning is used for a range of different types of human learning situations.

Users may accept its value based on either their own experience of using the system or on studies that have compared its use with other learning approaches.

Principally these relate to the composition of the group and its time-bounded activities. Although the group comprised experts from Europe, Oceania and North America, the voices of Asia and other lands were missing. Also, machine learning is making huge strides as new applications emerge almost daily. There are both parallels and major differences between human deep learning and deep machine learning. As we educate our students about machine learning, they can be encouraged to find out more about their own mental processes. An increased coverage of basic elements of neuroscience starting in primary schools could support students’ developing understanding of both human learning and machine learning.
Artificial Intelligence examples of artificial intelligence in education

Recent research, discussed in this paper, suggests that children aged 11 upwards can undertake such activities but developing associated basic literacies including algorithmic literacy can start much earlier. The specific content and sequencing of such curricula are topics for future research and development as discussed later in this paper.

Artificial Intelligence

For example, in a tutoring system to teach elementary physics, such as mechanics, the system may present the theory and worked-out examples. This should then affect what is presented and what other questions are asked of the student.

Ubiquitious Artificial Intelligence

The best way to develop a truly intelligent system is to use the known properties of the only intelligent system that we know: humans. Intelligent techniques are playing an increasingly important role in engineering and science having evolved from a specialized research subject to mainstream applied research and commercial products.

Manufacturing systems in industries has dramatically changed as a result of advanced manufacturing technologies employed in today’s factory. Factories are now trying to attend and maintain a world-class status through automation that is possible by sophisticated computer programs. The development of CAD/CAM system is evolving towards the phase of intelligent manufacturing system.

A tremendous amount of manufacturing knowledge is needed in an intelligent manufacturing system. Artificial intelligence techniques are designed for capturing, representing, organizing, and utilizing knowledge by computers, and hence play an important role in intelligent manufacturing. Artificial intelligence has provided several techniques with applications in manufacturing like; expert systems, artificial neural networks, genetic algorithms and fuzzy logic.

A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. When this turned out not to be so, there were many disappointing results. In the present state of AI, this has to be true.

Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition. Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. AI is more S/W related so the game can be easier or harder.Banks use intelligent software applications to screen and analyze financial data.

Research Methods for Education With Technology: Four Concerns, Examples, and Recommendations

The success of education with technology research is in part because the field draws upon theories and methods from multiple disciplines. However, drawing upon multiple disciplines has drawbacks because sometimes the methodological expertise of each discipline is not applied when researchers conduct studies outside of their research training.

The focus here is on research using methods drawn largely from psychology, for example, evaluating the impact of different systems on how students perform. The methodological concerns discussed are: low power; not using multilevel modeling; dichotomization; and inaccurate reporting of the numeric statistics.

The methods—both the design of the study and the statistical procedures—were examined for concerns that a reviewer might raise. These were chosen both by how much they may affect the conclusions and how easily they can be addressed. While these comments are critical, the purpose of the paper is to be constructive for the field. These were picked because of how well they illustrate the concern. Before doing this, some background on hypothesis testing is worth providing. Some statistical knowledge about this procedure is assumed in this discussion.
Artificial Intelligence examples of artificial intelligence in education

The choice of MED is sometimes influenced by the observed effects from similar studies. However, if you are confident that your true effect size is X, then there is no reason for the study.

The first was chosen because it uses a common, but much criticized, procedure called a median split. The second example involves the authors using a complex method to dichotomize the data.

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The easiest of these to address is errors in the numbers reported in tables and statistical reports. These types of errors will always be part of any literature, but it is important to lessen their likelihoods.

There is no reason to think that this was a deliberate fabrication.

About Education Works

The author starts by mentioning a few examples of how AI is/ may be used outside of education – both risky and useful.

Artificial Intelligence examples of artificial intelligence in education

It’s not hard to see how this could apply in educational contexts. There are probably ways to prevent this, but awareness of potential bias is needed for this to be done. All this considered, educators seeing AI as a tool that is free of bias would be rather worrying.

For this meeting, I read ‘How Artificial Intelligence Can Change Higher Education,’ a profile of Sebastian Thrun. The article detailed Thrun’s involvement with the popularization of massive open online courses and the founding of his company, Udacity.

At the heart of this question is the role that autonomous systems might have in helping to manage this kind of large scale educational system.

Like in the hype surrounding self-driving cars, the promises for a new educational paradigm that were put forward in this 2012 article still seem far off.

This short piece talked about how Robots are now being used for educational purposes and which ones are being used. The article talked a lot about how Robots can be used in a way that enhances learning by learning things themselves.

There were some aspects of the article that did make some sense on how Robots could aid learning, but these ideas didn’t go into much depth. It was discussed how Robots could talk in several languages so could be able to converse comfortably with a wider range of students. It also talked about how Robots could act as mediators to students, being able to check in, or provide advice at any time of the day.

As mentioned in the article ‘many people have an inherent distrust of advancing technologies.’ There are several questions to ask on how much a Robot is integrated into a learning environment, and when does it become too much.

They created a robot to ‘reduce loneliness and social isolation through warm technology’. AVI was a Robot created for children who are too ill to go to school. The robot sits in the class and the child at home can connect through it. Using an app, the children can take part in the classroom. They can raise their hand to answer questions, talk to nearby students, ask questions, and just listen if they want to.

Virtual studios are also described as “hubs”, an idea I would have liked to explore further. I wanted to know how a hub is different from a community.

First, it focused on place based learning as not being solely the province of lessons conducted in the field, away from the classroom.

As students explore their local communities, they can both explore critical issues facing the community and build on their own experiences in order to support their learning.

An important development in place based learning has been the rise in the ubiquity of smartphones and other location-aware devices. By tapping into GPS and other forms of location networks, it becomes possible to develop applications that allow learners to dynamically access information about their surroundings.

In this programme, children between 5-13 years old get visits in their school class every 3 weeks from a local baby, their parent and a Roots of Empathy instructor. The children observe how the baby and its feelings develop and its interactions with the parent. The curriculum is broken down into themes, which are then broken down further into age ranges. While the activities focus on feelings, some use knowledge and skills from school subjects, e.

Michael read ‘Using learning analytics to scale the provision of personalized feedback,’ a paper by Abelardo Pardo, Jelena Jovanovic, Shane Dawson, Dragan Gasevic and Negin Mirriahi. As it was designed, the system allowed instructors to create small, one or two sentence pieces of feedback for each activity within a course. Based on these, each week students would be able to receive a set of ‘personalized’ feedback that responded to their level of participation.

In the study, the authors found an improvement in student satisfaction with the feedback they received, but only a marginal improvement in performance, as compared to previous years. First, it was admirable in the way that it sought to use learning analytics techniques to improve feedback in large courses.

For world 1 , human led and closed, I was concerned that lots was only available to “higher paying students” and there was no mention at all of collaborative learning.

I liked it because it gave both pros and cons in a concise way.

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Emerging technologies: artificial intelligence

Thus all the general affordances of computing in education set out in Section 5 of this chapter will apply to AI. This section aims to tease out the extra potential that AI can offer in teaching and learning.

every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.

Indeed, so-called intelligent tutoring systems, automated multiple-choice test marking, and automated feedback on such tests have been around since the early 1980s. The closest to modern AI applications appear to be automated essay grading of standardised tests administered across an entire education system.

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It could be argued that all AI does is to encapsulate the existing biases in the system. The problem though is that this bias is often hard to detect in any specific algorithm, and that AI tends to scale up or magnify such biases.

In terms of what AI is actually doing now for teaching and learning, the dream is way beyond the reality. What works well in finance or marketing or astronomy does not necessarily translate to teaching and learning contexts. In doing the research for this section, it proved very difficult to find any compelling examples of AI for teaching and learning, compared with serious games or virtual reality.

This is mainly due to the difficulty of applying ‘modern’ AI at scale in a very fragmented system that relies heavily on relatively small class sizes, programs, and institutions. Probably for modern AI to ‘work’, a totally different organizational structure for teaching and learning would be needed.

Students often learn better when they feel that the instructor or teacher cares. In particular, students want to be treated as individuals, with their own interests, ways of learning, and some sense of control over their learning. Because of these emotional and personal aspects of learning, students need to relate in some way to their teacher or instructor. Learning is a complex activity where only a relatively minor part of the process can be effectively automated. Learning is an intensely human activity, that benefits enormously from personal relationships and social interaction.

However, to develop the skills and knowledge needed in a digital age, a more constructivist approach to learning is needed.

AI advocates often argue that they are not trying to replace teachers but to make their life easier or more efficient. The key driver of AI applications is cost-reduction, which means reducing the number of teachers, as this is the main cost in education.

Another problem with artificial intelligence is that the same old hype keeps going round and round. The same arguments for using artificial intelligence in education go back to the 1980s.

The trick though is to recognise exactly what kind of applications these new AI developments are good for, and what they cannot do well. In other words, the context in which AI is used matters, and needs to be taken account of.

Knowledge of and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students

At one medical school, the survey was sent out in a newsletter to the MD student body. At all schools, the survey was open for four weeks, with a reminder email sent two weeks after the first email. Participation was voluntary and was not related to the students’ ongoing curricular activities. Students were offered entry into a gift card raffle for completing the survey. Consent for study participation was obtained through the first page of the survey, and respondent anonymity was guaranteed by design.

Students’ perceptions of AI’s potential capability in the domains of individual health, health systems, and population health are described in Supplementary Table 1. Perceptions regarding the timeline in which these capabilities will be achieved are described in Supplementary Table 2.

Students were also concerned about how AI will affect the medical job market. They believe AI will raise ethical and social implications yet are unconvinced that our health system is equipped to deal with these novel challenges. Overall, students agree that medical education must do more to prepare students for the impact of AI in medicine.

Quo Vadis, Artificial Intelligence?

Rapid advances in research and technology in various fields have created environments into which artificial intelligence could embed itself naturally and comfortably. The scope for artificial intelligence in neuroscience and systems biology is extremely wide.

The forthcoming sections investigate how AI is situated in this extended environment. Initially, Section 2 takes a closer look at the interplay between AI, neuroscience, synthetic biology, and systems biology.

A feature that unites systems biology and synthetic biology is the tremendous complexity that is inherent in both fields.

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

Methods: This scoping review followed a previously published protocol from April 2020. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI.

Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education.

Additionally, as the majority of papers included in this review were expository papers and white papers, there is a need for more research in this context. Further research is needed to continue identifying the educational requirements and core competencies necessary for specifically integrating AIHTs into nursing practice.

Nurse educators in clinical practice and academic institutions around the world have an essential leadership role in preparing nurses and nursing students for the future state of AIHTs.

To our knowledge, this is the first scoping review to examine AIHTs and their influence on nursing education. While there has been research conducted on AIHTs and on nursing education as separate research topics, now is the time to realize the critical relationship between these two entities. AIHTs cannot be implemented in an effective manner without the solid foundation of nursing education, in both academic and clinical practice settings.

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

In this contribution, we have examined the ethical dimensions affected by the application of algorithm-driven decision-making.

As noted by a perceptive reviewer, ML systems that keep learning are dangerous and hard to understand because they can quickly change. Thus, could a ML system with real world consequences be “locked down” to increase transparency? If yes, the algorithm could become defective. If not, transparency today may not be helpful in understanding what the system does tomorrow. This issue could be tackled by hard-coding the set of rules on the behaviour of the algorithm, once these are agreed upon among the involved stakeholders. This would prevent the algorithm-learning process from conflicting with the standards agreed.

Thomas Hodgson, Jill Walter Rettberg, Elizabeth Chatterjee, Ragnar Fjelland and Marta Kuc-Czarnecka for their useful comments in this venue.

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Another application of AI was significantly noted in diagnostic imaging departments. Offering remote clinics with restricted resources access to tools for reading imaging needed for active clinical interventions. Feeding into these AI systems is a wealth of comparative studies to predict and describe abnormal studies, and enhance its predictions.

Additionally, AI has been used in monitoring patient’s vitals, and predicting deteriorating clinical course, requiring early resource utilization and critical decision making in a timely manner.

I personally have always had a utopian vision of how far health informatics can take our clinical practice, specifically EM.

What is Learning Analytics?

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Although machine learning is a field within computer science, it differs from traditional computational approaches. In traditional computing, algorithms are sets of explicitly programmed instructions used by computers to calculate or problem solve. Machine learning algorithms instead allow for computers to train on data inputs and use statistical analysis in order to output values that fall within a specific range.

We’ll explore which programming languages are most used in machine learning, providing you with some of the positive and negative attributes of each.

In supervised learning, the computer is provided with example inputs that are labeled with their desired outputs.

For example, with supervised learning, an algorithm may be fed data with images of sharks labeled as fish and images of oceans labeled as water.

A common use case of supervised learning is to use historical data to predict statistically likely future events. It may use historical stock market information to anticipate upcoming fluctuations, or be employed to filter out spam emails.

Unsupervised learning is often used for anomaly detection including for fraudulent credit card purchases, and recommender systems that recommend what products to buy next.

Correlation is a measure of association between two variables that are not designated as either dependent or independent. Regression at a basic level is used to examine the relationship between one dependent and one independent variable.

The k-nearest neighbor algorithm is a pattern recognition model that can be used for classification as well as regression. Often abbreviated as k-NN, the k in k-nearest neighbor is a positive integer, which is typically small.

In the simplified decision tree above, an example is classified by sorting it through the tree to the appropriate leaf node. This then returns the classification associated with the particular leaf, which in this case is either a Yes or a No.

A true classification tree data set would have a lot more features than what is outlined above, but relationships should be straightforward to determine.

Questions for Future Research

This access often allows certain apps, webpages, or extensions to be blocked to protect student information, which helps minimize the risk of data and/or security breaches.

Assistive technologies that use a form of AI may increase student engagement more than assistive technologies that do not include an AI component.

Teachers can help students protect their personal data by ensuring that personal profiles — to which educational technology companies have access — contain as little identifiable information as possible. Parental support for the use of assistive technologies could also be obtained, and school divisions could generate student log-in information that does not expose students’ identities. Students using personal devices should take additional measures to ensure that their privacy and security is maintained.

Allowing students to choose the assistive technology tools that could help them achieve their educational goals can promote greater independence and autonomy.

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