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Inquiry and critical thinking skills for the next generation: from artificial intelligence back to human intelligence



Abstract Along with the increasing attention to artificial intelligence (AI), renewed emphasis or reflection on human intelligence (HI) is appearing in many places and at multiple levels. One of the foci is critical thinking. Critical thinking is one of four key 21st century skills – communication, collaboration, critical thinking and creativity. Though most people are aware of the value of critical thinking, it lacks emphasis in curricula. In this paper, we present a comprehensive definition of critical thinking that ranges from observation and inquiry to argumentation and reflection. Given a broad conception of critical thinking, a developmental approach beginning with children is suggested as a way to help develop critical thinking habits of mind. The conclusion of this analysis is that more emphasis should be placed on developing human intelligence, especially in young children and with the support of artificial intelligence. While much funding and support goes to the development of artificial intelligence, this should not happen at the expense of human intelligence. Overall, the purpose of this paper is to argue for more attention to the development of human intelligence with an emphasis on critical thinking.
C O M M E N T A R Y Open Access
Inquiry and critical thinking skills for the
next generation: from artificial intelligence
back to human intelligence
Jonathan Michael Spector
and Shanshan Ma
* Correspondence: Mike.Spector@
Department of Learning
Technologies, University of North
Texas Denton, Texas, TX 76207, USA
Along with the increasing attention to artificial intelligence (AI), renewed emphasis
or reflection on human intelligence (HI) is appearing in many places and at multiple
levels. One of the foci is critical thinking. Critical thinking is one of four key 21st
century skills communication, collaboration, critical thinking and creativity. Though
most people are aware of the value of critical thinking, it lacks emphasis in curricula.
In this paper, we present a comprehensive definition of critical thinking that ranges
from observation and inquiry to argumentation and reflection. Given a broad
conception of critical thinking, a developmental approach beginning with children is
suggested as a way to help develop critical thinking habits of mind. The conclusion
of this analysis is that more emphasis should be placed on developing human
intelligence, especially in young children and with the support of artificial
intelligence. While much funding and support goes to the development of artificial
intelligence, this should not happen at the expense of human intelligence. Overall,
the purpose of this paper is to argue for more attention to the development of
human intelligence with an emphasis on critical thinking.
Keywords: Artificial intelligence, Critical thinking, Developmental model, Human
intelligence, Inquiry learning
In recent decades, advancements in Artificial Intelligence (AI) have developed at an in-
credible rate. AI has penetrated into peoples daily life on a variety of levels such as
smart homes, personalized healthcare, security systems, self-service stores, and online
shopping. One notable AI achievement was when AlphaGo, a computer program,
defeated the World Go Champion Mr. Lee Sedol in 2016. In the previous year,
AlphaGo won in a competition against a professional Go player (Silver et al. 2016). As
Go is one of the most challenging games, the wins of AI indicated a breakthrough.
Public attention has been further drawn to AI since then, and AlphaGo continues to
improve. In 2017, a new version of AlphaGo beat Ke Jie, the current world No.1 rank-
ing Go player. Clearly AI can manage high levels of complexity.
Given many changes and multiple lines of development and implement, it is somewhat
difficult to define AI to include all of the changes since the 1980s (Luckin et al. 2016).
Many definitions incorporate two dimensions as a starting point: (a) human-like thinking,
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Smart Learnin
Spector and Ma Smart Learning Environments (2019) 6:8
and (b) rational action (Russell and Norvig 2009). Basically, AI is a term used to label ma-
chines (computers) that imitate human cognitive functions such as learning and problem
solving, or that manage to deal with complexity as well as human experts.
AlphaGos wins against human players were seen as a comparison between artificial
and human intelligence. One concern is that AI has already surpassed HI; other con-
cerns are that AI will replace humans in some settings or that AI will become uncon-
trollable (Epstein 2016; Fang et al. 2018). Scholars worry that AI technology in the
future might trigger the singularity (Good 1966), a hypothesized future that the devel-
opment of technology becomes uncontrollable and irreversible, resulting in unfathom-
able changes to human civilization (Vinge 1993).
The famous theoretical physicist Stephen Hawking warned that AI might end
mankind, yet the technology he used to communicate involved a basic form of AI
(Cellan-Jones 2014). This example highlights one of the basic dilemmas of AI
namely, what are the overall benefits of AI versus its potential drawbacks, and how
to move forward given its rapid development? Obviously, basic or controllable AI
technologies are not what people are afraid of. Spector et al. 1993distinguished
strong AI and weak AI. Strong AI involves an application that is intended to replace
an activity performed previously by a competent human, while weak AI involves an
application that aims to enable a less experienced human to perform at a much
higher level. Other researchers categorize AI into three levels: (a) artificial narrow
intelligence (Narrow AI), (b) artificial general intelligence (General AI), and (c)
artificial super intelligence (Super AI) (Siau and Yang 2017;ZhangandXie2018).
Narrow AI, sometimes called weak AI, refers to a computer that focus on a narrow
task such as AlphaZero or a self-driving car. General AI, sometimes referred to as
strong AI, is the simulation of human-level intelligence, which can perform more
cognitive tasks as well as most humans do. Super AI is defined by Bostrom (1998)
as an intellect that is much smarter than the best human brains in practically every
field, including scientific creativity, general wisdom and social skills(p.1).
Although the consequence of singularity and its potential benefits or harm to the hu-
man race have been intensely debated, an undeniable fact is that AI is capable of
undertaking recursive self-improvement. With the increasing improvement of this cap-
ability, more intelligent generations of AI will appear rapidly. On the other hand, HI
has its own limits and its development requires continuous efforts and investment from
generation to generation. Education is the main approach humans use to develop and
improve HI. Given the extraordinary growth gap between AI and HI, eventually AI can
surpass HI. However, that is no reason to neglect the development and improvement
of HI. In addition, in contrast to the slow development rate of HI, the growth of fund-
ing support to AI has been rapidly increasing according to the following comparison of
support for artificial and human intelligence.
The funding support for artificial and human intelligence
There are challenges in comparing artificial and human intelligence by identifying
funding for both. Both terms are somewhat vague and can include a variety of aspects.
Some analyses will include big data and data analytics within the sphere of artificial
intelligence and others will treat them separately. Some will include early childhood
Spector and Ma Smart Learning Environments (2019) 6:8 Page 2 of 11
developmental research within the sphere of support for HI and others treat them sep-
arately. Education is a major way of human beings to develop and improve HI. The in-
vestments in education reflect the efforts put on the development of HI, and they pale
in comparison with investments in AI.
Sources also vary from governmental funding of research and development to business
and industry investments in related researchanddevelopment.Nonetheless, there are
strong indications of increased funding support for AI in North America, Europe and Asia,
especially in China. The growth in funding for AI around the world is explosive. According
to ZDNet, AI funding more than doubled from 2016 to 2017 and more than tripled from
2016 to 2018. The growth in funding for AI in the last 10 years has been exponential.
According to Venture Scanner, there are approximately 2500 companies that have raised
$60 billion in funding from 3400 investors in 72 different countries (see https://www.slide- Areas included
in the Venture Scanner analysis included virtual assistants, recommendation engines, video
recognition, context-aware computing, speech recognition, natural language processing, ma-
chine learning, and more.
The above data on AI funding focuses primarily on companies making products.
There is no direct counterpart in the area of HI where the emphasis is on learning
and education. What can be seen, however, are trends within each area. The above
data suggest exponential growth in support for AI. In contrast, according to the
Urban Institute, per-student funding in the USA has been relatively flat for nearly
two decades, with a few states showing modest increases and others showing none
(see Funding for educa-
tion is complicated due to the various sources. In the USA, there are local, state and
federal sources to consider. While that mixture of funding sources is complex, it is
clear that federal and state spending for education in the USA experienced an in-
crease after World War II. However, since the 1980s, federal spending for education
has steadily declined, and state spending on education in most states has declined
since 2010 according to a government report (see https://www.usgovernmentspend- This decline in funding reflects the decreasing em-
phasis on the development of HI, which is a dangerous signal.
Decreased support for education funding in the USA is not typical of what is happen-
ing in other countries, according to The Hechinger Report (see https://hechingerreport.
org/rest-world-invests-education-u-s-spends-less/). For example, in the period of 2010
to 2014, American spending on elementary and high school education declined 3%,
whereas in the same period, education spending in the 35 countries in the OECD rose
by 5% with some countries experiencing very significant increases (e.g., 76% in Turkey).
Such data can be questioned in terms of how effectively funds are being spent or how
poorly a country was doing prior to experiencing a significant increase. However, given the
performance of American students on the Program for International Student Assessment
(PISA), the relative lack of funding support in theUSAisroughlyrelatedwiththemediocre
performance on PISA tests (see
Research by Darling-Hammond (2014) indicated that in order to improve learning and re-
duce the achievement gap, systematic government investments in high-need schools would
be more effective if the focus was on capacity building, improving the knowledge and skills
of educators and the quality of curriculum opportunities.
Spector and Ma Smart Learning Environments (2019) 6:8 Page 3 of 11
Though HI could not be simply defined by the performance on PISA test, improving
HI requires systematic efforts and funding support in high-need areas as well. So, in
the following section, we present a reflection on HI.
Reflection on human intelligence
Though there is a variety of definitions of HI, from the perspective of psychology, ac-
cording to Sternberg (1999), intelligence is a form of developing expertise, from a nov-
ice or less experienced person to an expert or more experienced person, a student
must be through multiple learning (implicit and explicit) and thinking (critical and cre-
ative) processes. In this paper, we adopted such a view and reflected on HI in the fol-
lowing section by discussing learning and critical thinking.
What is learning?
We begin with Gagnés(1985) definition of learning as characterized by stable and
persistent changes in what a person knows or can do. How do humans learn? Do
you recall how to prove that the square root of 2 is not a rational number, some-
thing you might have learned years ago? The method is intriguing and is called an
indirect proof or a reduction to absurdity assume that the square root of 2 is a ra-
tional number and then apply truth preserving rules to arrive at a contradiction to
show that the square root of 2 cannot be a rational number. We recommend this as
an exercise for those readers who have never encountered that method of learning
and proof. (see
diction). Yet another interesting method of learning is called the process of elimin-
ation, sometimes accredited to Arthur Conan Doyles(1926)inThe Adventure of
the Blanched Soldier Sherlock Holmes says to Dr. Watson that the process of
elimination starts upon the supposition that when you have eliminated all which is
impossible, that whatever remains, however improbable, must be the truth(see
The reason to mention Sherlock Holmes early in this paper is to emphasize the role
that observation plays in learning. The character Sherlock Holmes was famous for his
observation skills that led to his so-called method of deductive reasoning (a process of
elimination), which is what logicians would classify as inductive reasoning as the con-
clusions of that reasoning process are primarily probabilistic rather than certain, unlike
the proof of the irrationality of the square root of 2 mentioned previously.
In dealing with uncertainty, it seems necessary to make observations and gather evi-
dence that can lead one to a likely conclusion. Is that not what reasonable people and
accomplished detectives do? It is certainly what card counters do at gambling houses;
they observe high and low value cards that have already been played in order to esti-
mate the likelihood of the next card being a high or low value card. Observation is a
critical process in dealing with uncertainty.
Moreover, humans typically encounter many uncertain situations in the course of life.
Few people encounter situations which require resolution using a mathematical proof
such as the one with which this article began. Jonassen (2000,2011) argued that problem
solving is one of the most important and frequent activities in which people engage.
Spector and Ma Smart Learning Environments (2019) 6:8 Page 4 of 11
Moreover, many of the more challenging problems are ill-structured in the sense that (a)
there is incomplete information pertaining to the situation, or (b) the ideal resolution of
the problem is unknown, or (c) how to transform a problematic situation into an accept-
able situation is unclear. In short, people are confronted with uncertainty nearly every day
and in many different ways. The so called key 21st century skills of communication, col-
laboration, critical thinking and creativity (the 4 Cs; see
networks/p21) are important because uncertainty is a natural and inescapable aspect of
the human condition. The 4 Cs are interrelated and have been presented by Spector
(2018) as interrelated capabilities involving logic and epistemology in the form of the new
3Rs namely, re-examining, reasoning, and reflecting. Re-examining is directly linked to
observation as a beginning point for inquiry. The method of elimination is one form of
reasoning in which a person engages to solve challenging problems. Reflecting on how
well one is doing in the life-long enterprise of solving challenging problems is a higher
kind of meta-cognitive activity in which accomplished problem-solvers engage (Ericsson
et al. 1993;Flavell1979).
Based on these initial comments, a comprehensive definition of critical thinking is
presented next in the form of a framework.
A framework of critical thinking
Though there is variety of definitions of critical thinking, a concise definition of
critical thinking remains elusive. For delivering a direct understanding of critical
thinking to readers such as parents and school teachers, in this paper, we present a
comprehensive definition of critical thinking through a framework that includes
many of the definitions offered by others. Critical thinking, as treated broadly
herein, is a multi-dimensioned and multifaceted human capability. Critical thinking
has been interpreted from three perspectives: education, psychology, and epistemol-
ogy, all of which are represented in the framework that follows.
In a developmental approach to critical thinking, Spector (2019) argues that critical
thinking involves a series of cumulative and related abilities, dispositions and other var-
iables (e.g., motivation, criteria, context, knowledge). This approach proceeds from ex-
perience (e.g., observing something unusual) and then to various forms of inquiry,
investigation, examination of evidence, exploration of alternatives, argumentation, test-
ing conclusions, rethinking assumptions, and reflecting on the entire process.
Experience and engagement are ongoing throughout the process which proceeds from
relatively simple experiences (e.g., direct and immediate observation) to more complex in-
teractions (e.g., manipulation of an actual or virtual artifact and observing effects).
The developmental approach involves a variety of mental processes and non-cogni-
tive states, which help a persons decision making to become purposeful and goal di-
rected. The associated critical thinking skills enable individuals to be likely to achieve a
desired outcome in a challenging situation.
In the process of critical thinking, apart from experience, there are two additional
cognitive capabilities essential to critical thinking namely, metacognition and self-
regulation. Many researchers (e.g., Schraw et al. 2006) believe that metacognition
has two components: (a) awareness and understanding of ones own thoughts, and
(b) the ability to regulate ones own cognitive processes. Some other researchers put
Spector and Ma Smart Learning Environments (2019) 6:8 Page 5 of 11
more emphasis on the latter component. For example, Davies (2015) described
metacognition as the capacity to monitor the quality of ones thinking process, and
then to make appropriate changes. However, the American Psychology Association
(APA) defines metacognition as an awareness and understanding of onesown
thought with the ability to control related cognitive processes (see https://psycnet.
Although the definition and elaboration of these two concepts deserve further explor-
ation, they are often used interchangeably (Hofer and Sinatra 2010; Schunk 2008).
Many psychologists see the two related capabilities of metacognition and self-regulation
as being closely related - two sides on one coin, so to speak. Metacognition involves or
emphasizes awareness, whereas self-regulation involves and emphasizes appropriate
control. These two concepts taken together enable a person to create a self-regulatory
mechanism, which monitors and regulates the corresponding skills (e.g., observation,
inquiry, interpretation, explanation, reasoning, analysis, evaluation, synthesis, reflection,
and judgement).
As to the critical thinking skills, it should be noted that there is much discussion about
the generalizability and domain specificity of them, just as there is about problem-solving
skills in general (Chi et al. 1982; Chiesi et al. 1979;Ennis1989;Fischer1980). The re-
search supports the notion that to achieve high levels of expertise and performance, one
must develop high levels of domain knowledge. As a consequence, becoming a highly ef-
fective critical thinker in a particular domain of inquiry requires significant domain know-
ledge. One may achieve such levels in a domain in which one has significant domain
knowledge and experience but not in a different domain in which one has little domain
knowledge and experience. The processes involved in developing high levels of critical
thinking are somewhat generic. Therefore, it is possible to develop critical thinking in
nearly any domain when the two additional capabilities of metacognition and self-regula-
tion are coupled with motivation and engagement and supportive emotional states (Erics-
son et al. 1993).
Consequently, the framework presented here (see Fig. 1) is built around three main
perspectives about critical thinking (i.e., educational, psychological and epistemological)
and relevant learning theories. This framework provides a visual presentation of critical
thinking with four dimensions: abilities (educational perspective), dispositions (psycho-
logical perspective), levels (epistemological perspective) and time. Time is added to
emphasize the dynamic nature of critical thinking in terms of a specific context and a
developmental approach.
Critical thinking often begins with simple experiences such as observing a difference,
encountering a puzzling question or problem, questioning someones statement, and
then leads, in some instances to an inquiry, and then to more complex experiences
such as interactions and application of higher order thinking skills (e.g., logical reason-
ing, questioning assumptions, considering and evaluating alternative explanations).
If the individual is not interested in what was observed, an inquiry typically does not
begin. Inquiry and critical thinking require motivation along with an inquisitive dispos-
ition. The process of critical thinking requires the support of corresponding internal in-
dispositions such as open-mindedness and truth-seeking. Consequently, a disposition
to initiate an inquiry (e.g., curiosity) along with an internal inquisitive disposition (e.g.,
that links a mental habit to something motivating to the individual) are both required
Spector and Ma Smart Learning Environments (2019) 6:8 Page 6 of 11
(Hitchcock 2018). Initiating dispositions are those that contribute to the start of inquiry
and critical thinking. Internal dispositions are those that initiate and support corre-
sponding critical thinking skills during the process. Therefore, critical thinking disposi-
tions consist of initiating dispositions and internal dispositions. Besides these factors,
critical thinking also involves motivation. Motivation and dispositions are not mutually
exclusive, for example, curiosity is a disposition and also a motivation.
Critical thinking abilities and dispositions are two main components of critical
thinking, which involve such interrelated cognitive constructs as interpretation, ex-
planation, reasoning, evaluation, synthesis, reflection, judgement, metacognition and
self-regulation (Dwyer et al. 2014;Davies2015; Ennis 2018; Facione 1990; Hitchcock
2018;PaulandElder2006). There are also some other abilities such as communica-
tion, collaboration and creativity, which are now essential in current society (see Those abilities along with critical
thinking are called the 4Cs; they are individually monitored and regulated through
metacognitive and self-regulation processes.
The abilities involved in critical thinking are categorized in Blooms taxonomy into
higher order skills (e.g., analyzing and synthesizing) and lower level skills (e.g., remem-
bering and applying) (Anderson and Krathwohl 2001; Bloom et al. 1956).
The thinking process can be depicted as a spiral through both lower and higher order
thinking skills. It encompasses several reasoning loops. Some of them might be iterative
until a desired outcome is achieved. Each loop might be a mix of higher order thinking
skills and lower level thinking skills. Each loop is subject to the self-regulatory mechan-
ism of metacognition and self-regulation.
But, due to the complexity of human thinking, a specific spiral with reasoning loops
is difficult to represent. Therefore, instead of a visualized spiral with an indefinite num-
ber of reasoning loops, the developmental stages of critical thinking are presented in
the diagram (Fig. 1).
Fig. 1 A framework of critical thinking
Spector and Ma Smart Learning Environments (2019) 6:8 Page 7 of 11
Besides, most of the definitions of critical thinking are based on the imagination about ideal
critical thinkers such as the consensus generated from the Delphi report (Facione 1990).
However, according to Dreyfus and Dreyfus (1980),inthecourseofdevelopinganexpertise,
students would pass through five stages. Those five stages are absolute beginner,advanced
beginner,competent performer,proficient performer,and intuitive expert performer.
Dreyfus and Dreyfus (1980) described the five stages the result of the successive transforma-
tions of four mental functions: recollection, recognition, decision making, and awareness.
In the course of developing critical thinking and expertise, individuals will pass
through similar stages which are accompanied with the increasing practices and accu-
mulation of experience. Through the intervention and experience of developing critical
thinking, as a novice, tasks are decomposed into context-free features which could be
recognized by students without the experience of particular situations. For further im-
proving, students need to be able to monitor their awareness, and with a considerable
experience. They can note recurrent meaningful component patterns in some contexts.
Gradually, increased practices expose students to a variety of whole situations which
enable the students to recognize tasks in a more holistic manner as a professional. On
the other hand, with the increasing accumulation of experience, individuals are less
likely to depend simply on abstract principles. The decision will turn to something in-
tuitive and highly situational as well as analytical. Students might unconsciously apply
rules, principles or abilities. A high level of awareness is absorbed. At this stage, critical
thinking is turned into habits of mind and in some cases expertise. The description
above presents a process of critical thinking development evolving from a novice to an
expert, eventually developing critical thinking into habits of mind.
We mention the five-stage model proposed by Dreyfus and Dreyfus (1980)to
categorize levels of critical thinking and emphasize the developmental nature involved
in becoming a critical thinker. Correspondingly, critical thinking is categorized into 5
levels: absolute beginner (novice), advanced beginner (beginner), competent performer
(competent), proficient performer (proficient), and intuitive expert (expert).
Ability level and critical thinker (critical thinking) level together represent one of the
four dimensions represented in Fig. 1.
In addition, it is noteworthy that the other two elements of critical thinking are the
context and knowledge in which the inquiry is based. Contextual and domain know-
ledge must be taken into account with regard to critical thinking, as previously argued.
Besides, as Hitchcock (2018) argued, effective critical thinking requires knowledge
about and experience applying critical thinking concepts and principles as well.
Critical thinking is considered valuable across disciplines. But except few courses such
as philosophy, critical thinking is reported lacking in most school education. Most of
researchers and educators thus proclaim that integrating critical thinking across the
curriculum (Hatcher 2013). For example, Ennis (2018) provided a vision about incorp-
orating critical thinking across the curriculum in higher education. Though people are
aware of the value of critical thinking, few of them practice it. Between 2012 and 2015,
in Australia, the demand of critical thinking as one of the enterprise skills for early-car-
eer job increased 125% (Statista Research Department, 2016). According to a survey
across 1000 adults by The Reboot Foundation 2018, more than 80% of respondents
Spector and Ma Smart Learning Environments (2019) 6:8 Page 8 of 11
believed that critical thinking skills are lacking in todays youth. Respondents were
deeply concerned that schools do not teach critical thinking. Besides, the investigation
also found that respondents were split over when and how to teach critical thinking,
In the previous analysis of critical thinking, we presented the mechanism of critical
thinking instead of a concise definition. This is because, given the various perspectives of
interpreting critical thinking, it is not easy to come out with an unitary definition, but it is
essential for the public to understand how critical thinking works, the elements it involves
and the relationships between them, so they can achieve an explicit understanding.
In the framework, critical thinking starts from simple experience such as observing a
difference, then entering the stage of inquiry, inquiry does not necessarily turn the
thinking process into critical thinking unless the student enters a higher level of think-
ing process or reasoning loops such as re-examining, reasoning, reflection (3Rs). Being
an ideal critical thinker (or an expert) requires efforts and time.
According to the framework, simple abilities such as observational skills and inquiry are
indispensable to lead to critical thinking, which suggests that paying attention to those
simple skills at an early stage of children can be an entry point to critical thinking. Consid-
ering the child development theory by Piaget (1964), a developmental approach spanning
multiple years can be employed to help children develop critical thinking at each corre-
sponding development stage until critical thinking becomes habits of mind.
Although we emphasized critical thinking in this paper, for the improvement of
intelligence, creative thinking and critical thinking are separable, they are both essential abil-
ities that develop expertise, eventually drive the improvement of HI at human race level.
As previously argued, there is a similar pattern among students who think critically
in different domains, but students from different domains might perform differently in
creativity because of different thinking styles (Haller and Courvoisier 2010). Plus, stu-
dents have different learning styles and preferences. Personalized learning has been the
most appropriate approach to address those differences. Though the way of realizing
personalized learning varies along with the development of technologies. Generally,
personalized learning aims at customizing learning to accommodate diverse students
based on their strengths, needs, interests, preferences, and abilities.
Meanwhile, the advancement of technology including AI is revolutionizing education; stu-
dentslearning environments are shifting from technology-enhanced learning environments
to smart learning environments. Although lots of potentials are unrealized yet (Spector
2016), the so-called smart learning environments rely more on the support of AI technology
such as neural networks, learning analytics and natural language processing. Personalized
learning is better supported and realized in a smart learning environment. In short, in the
current era, personalized learning is to use AI to help learners perform at a higher level mak-
ing adjustments based on differences of learners. This is the notion with which we conclude
the future lies in using AI to improve HI and accommodating individual differences.
The application of AI in education has been a subject for decades. There are efforts
heading to such a direction though personalized learning is not technically involved in
them. For example, using AI technology to stimulate critical thinking (Zhu 2015), apply-
ing a virtual environment for building and assessing higher order inquiry skills (Ketelhut
et al. 2010). Developing computational thinking through robotics (Angeli and Valanides
2019) is another such promising application of AI to support the development of HI.
Spector and Ma Smart Learning Environments (2019) 6:8 Page 9 of 11
However, almost all of those efforts are limited to laboratory experiments. For accel-
erating the development rate of HI, we argue that more emphasis should be given to
the development of HI at scale with the support of AI, especially in young children fo-
cusing on critical and creative thinking.
In this paper, we argue that more emphasis should be given to HI development. Rather than
decreasing the funding of AI, the analysis of progress in artificial and human intelligence indi-
cates that it would be reasonable to see increased emphasis placed on using various AI tech-
niques and technologies to improve HI on a large and sustainable scale. Well, most
researchers might agree that AI techniques or the situation might be not mature enough to
support such a large-scale development. But it would be dangerous if HI development is
overlooked. Based on research and theory drawn from psychology as well as from epistemol-
ogy, the framework is intended to provide a practical guide to the progressive development
of inquiry and critical thinking skills in young children as children represent the future of our
fragile planet. And we suggested a sustainable development approach for developing inquiry
and critical thinking (See, Spector 2019). Such an approach could be realized through AI and
infused into HI development. Besides, a project is underway in collaboration with NetDragon
to develop gamified applications to develop the relevant skills and habits of mind. A game-
based assessment methodology is being developed and tested at East China Normal Univer-
sity that is appropriate for middle school children. The intention of the effort is to refocus
some of the attention on the development of HI in young children.
AI: Artificial Intelligence; HI: Human Intelligence
We wish to acknowledge the generous support of NetDragon and the Digital Research Centre at the University of
North Texas.
The authors contributed equally to the effort. Both authors read and approved the final manuscript.
Initial work is being funded through the NetDragon Digital Research Centre at the University of North Texas with
Author as the Principal Investigator.
Availability of data and materials
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Received: 6 June 2019 Accepted: 27 August 2019
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Spector and Ma Smart Learning Environments (2019) 6:8 Page 11 of 11
... These actions are best qualified as those requiring analytical skill. The results of this study therefore, allows conclusions to be made in alignment with the proposal that stated that first, it is important for human intelligence to be developed, especially in young children and with the support of artificial intelligence; second, communication, collaboration, critical thinking and creativity skills are skills that are necessary in today's world [26]. ...
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... Berpikir kritis adalah konsep yang kaya yang telah berkembang selama 2.500 tahun terakhir. Istilah berpikir kritis berakar pada pertengahan akhir abad ke-20 (Spector, J. M., & Ma, 2019). Definisi yang tumpang tindih yang bersama-sama membentuk konsepsi pemikiran kritis yang substantif dan transdisipliner. ...
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... Limited work has been done on the assessment of higher-order thinking skills, analysis of learning patterns, and investigating methods that allow the use of AI. At the same time, researchers (Spector & Ma, 2019) have warned about over-emphasis on the use of AI and the need to rely on human intelligence, and the development and assessment of higher-order thinking skills in learners through simulations and games (Ketelhut et al., 2010;Van Voorhis & Paris, 2019). ...
... Critical thinking is not just thinking rooted in reason but includes the ability to consider multiple perspectives, be informed, be skeptical, and require valid reasoning (Mason, 2007). Thus critical thinking exists in three dimensions, namely education, psychology, and epistemology which contain elements of beliefs, desires, and values (Spector & Ma, 2019;Norris, 2014;Yanchar & Slife, 2008). Sternberg (2003) states these analytic, synthetic, and practical thinking elements as Triachic terms. ...
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In other words, in addition to being the engine behind much ‘smart’ ed tech, AIEd is also designed to be a powerful tool to open up what is sometimes called the ‘black box of learning,’ giving us more fine-grained understandings of how learning actually happens. Although some might find the concept of AIEd alienating, the algorithms and models that underpin ed tech powered by AIEd form the basis of an essentially human endeavor. Using AIEd, teachers will be able to offer learners educational experiences that are more personalised, flexible, inclusive and engaging. Crucially, we do not see a future in which AIEd replaces teachers. What we do see is a future in which the extraordinary expertise of teachers is better leveraged and augmented through the thoughtful deployment of well designed AIEd. We have available, right now, AIEd tools that could support student learning at a scale previously unimaginable by providing one-on-one tutoring to every student, in every subject. Existing technologies also have the capacity to provide intelligent support to learners working in a group, and to create authentic virtual learning environments where students have the right support, at the right time, to tackle real-life problems and puzzles. In the near future, we expect that teaching and learning will increasingly be supported by the thoughtful application of AIEd tools. For example, by lifelong learning companions powered by AI that can accompany and support individual learners throughout their studies - in and beyond school - and new forms of assessment that measure learning while it is taking place, shaping the learning experience in real time. If we are ultimately successful, we predict that AIEd will help us address some of the most intractable problems in education, including achievement gaps and teacher retention. AIEd will also help us respond to the most significant social challenge that AI has already brought - the steady replacement of jobs and occupations with clever algorithms and robots. It is our view that this provides a new innovation imperative in education, which can be expressed simply: as humans live and work alongside increasingly smart machines, our education systems will need to achieve at levels that none have managed to date. True progress will require the development of an AIEd infrastructure. This will not, however, be a single monolithic AIEd system. Instead, it will resemble the marketplace that has developed for smartphone apps: hundreds and then thousands of individual AIEd components, developed in collaboration with educators, conformed to uniform international data standards, and shared with researchers and developers worldwide. These standards will also enable system-level data collation and analysis that will help us to learn much more about learning itself – and how to improve it. Moving forward, we will need to pay close attention to three powerful forces as we map the future of artificial intelligence in education, namely pedagogy, technology, and system change. Paying attention to the pedagogy will mean that the design of new edtech should always start with what we know about learning. It also means that the system for funding this work must be simultaneously opened up and refocused, moving away from isolated pockets of R&D and toward collaborative enterprises that prioritise areas known to make a real difference to teaching and learning. Paying attention to the technology will mean creating smarter demand for commercial grade AIEd products that work. It also means the development of a robust, component-based AIEd infrastructure, similar to the smartphone app marketplace, where researchers and developers can access standardised components that have been developed in collaboration with educators. Paying attention to system change will mean involving teachers, students, and parents in co-designing new tools, so that AIEd will appropriately address the inherent “messiness” of real classroom, university, and workplace learning environments. It also means the development of data standards that promote the safe and ethical use of data. Said succinctly, we need intelligent technologies that embody what we know about great teaching and learning, embodied in enticing consumer grade products, which are then used effectively in real-life settings that combine the best of human and machine. We do not underestimate the new-thinking, inevitable wrong-turns, and effort required to realise these recommendations. However, if we are to properly unleash the intelligence of AIEd, we must do things differently - via new collaborations, sensible funding, and (always) a keen eye on the pedagogy. The potential prize is too great to act otherwise.
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The game of Go has long been viewed as the most challenging of classic games for artificial intelligence owing to its enormous search space and the difficulty of evaluating board positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
The study examined the effects of learning with the Bee-Bot on young boys' and girls' computational thinking within the context of two scaffolding techniques. The study reports statistically significant learning gains between the initial and final assessment of children's computational thinking skills. Also, according to the findings, while both boys and girls benefited from the scaffolding techniques, a statistically significant interaction effect was detected between gender and scaffolding strategy showing that boys benefited more from the individualistic, kinesthetic, spatially-oriented, and manipulative-based activity with the cards, while girls benefited more from the collaborative writing activity. In regards to the children's problem-solving strategies during debugging, the results showed that the majority of them used decomposition as a strategy to deal with the complexity of the task. These results are important, because they show that children at this very young age are able to cope with the complexity of a learning task by decomposing it into a number of subtasks that are easier for them to tackle. The research contributes to the body of knowledge about the teaching of computational thinking. In addition, the study has practical significance for curriculum developers, instructional leaders, and classroom teachers, as they can use the results of this study to design curricula and classroom activities with a focus on the broader set of computational thinking skills, and not only coding.
Among the skill and competency areas being addressed in national education plans and by prominent educators are collaboration, communication, creativity, and critical thinking. In this chapter, a fifth C is added to that list—namely, contemplation. The argument to be presented in this chapter involves two assumptions: (a) technologies can play an important role in developing these competencies, and (b) to be effective in developing the five C’s, a holistic and developmental approach seems appropriate. Given those assumptions, the solution approach here is that a learner should be considered as a whole person and not simply a cognitive processor. Moreover, promoting effective learning involves developing stable and persistent changes in what a person knows and can do. Consequently, developing habits of inquiry and reasoning takes time and is not likely to happen in one unit of instruction, nor in one course. The earlier those habits are developed, the more likely they are to persist and to be applied to multiple domains of inquiry.