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Improvements in Artificial Intelligence and Its Impact on Child Psychology

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26 July 2023

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28 July 2023

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Abstract
Artificial Intelligence (AI) has become a hopeful tool for helping parents with different parts of raising children by giving them personalized tips, evidence-based methods, and timely solutions. This paper talks about the difficulties and possibilities of using AI to help parents. It also talks about the possible benefits and its impact on child psychology. There are a lot of ways that AI can help parents, from giving personalized advice that fits the needs of each family to intervening quickly when it spots early signs of developmental problems or parenting stress. AI makes it easier for parents to find tools, workshops, and interactive modules that are based on research. This gives parents the knowledge and trust they need to make choices about their child's growth that are based on facts. Also, AI-powered support can connect parents with similar problems, creating a sense of community and making it less embarrassing to ask for help with parenting or mental health. But there are a few problems that need to be carefully dealt with before AI can be used to help parents in a responsible and effective way. Data privacy and security are very important to protect families' safety and well-being. Important things to think about are the ethical use of data, transparency, and educated permission. Also, the accuracy and dependability of AI programs are very important when it comes to giving good advise and suggestions. To make sure everyone gets fair and equal help, efforts must be made to find and get rid of flaws. Also, AI shouldn't replace human sense and experience; it should add to them. When parents rely too much on AI, they might forget how important human contact and mental support are. To accommodate different parenting styles and views, it is important to be sensitive to culture and circumstances. In conclusion, AI-powered parenting support has a lot of promise to help parents be better parents and help children grow and learn. AI can be a useful tool for helping parents if it can solve problems related to data privacy, ethics, reducing bias, and working together with humans. Finding a balance between technology progress and human connection is important if AI is to be used to its fullest potential to give parents more power and help children grow in the best way possible.
Keywords: 
Subject: Social Sciences  -   Psychiatry and Mental Health

Introduction:

Artificial Intelligence (AI) is changing many areas, including schooling. In recent years, AI has shown that it has the ability to change early childhood education, which focuses on building a strong base for a child's learning journey that will last their whole lives. Educational tools and systems that use AI are made to meet the specific needs of young learners, making their learning experiences better and helping them grow as a whole. This piece goes into detail about how AI is improving early childhood education and what that means for the future as 1-5,30:
1. Individualized learning:
Educational tools that are powered by AI can change to fit each child's learning style and pace. AI programs can make personalized learning paths by looking at a child's skills, flaws, and learning style. This personalized method makes sure that young learners get content and tasks that match their skill levels. This makes learning more fun and effective.
2. Automatic Evaluation:
Traditional ways of assessing can take a long time and might not give you feedback right away. Assessment tools that are driven by AI can quickly look at a child's answers and success on different tasks, quizzes, or games. Educators can use this real-time data to find places where their lessons could be better and change them to make their lessons more effective.
3. Learning Assistants and Virtual Tutors:
Young students can connect with virtual teachers that are run by AI. These virtual figures have conversations with kids, answering their questions, explaining things, and giving them support. Virtual learning helpers can also keep track of a child's progress, make suggestions based on that child's needs, and change the level of difficulty of tasks as required.
4. Learning a Language and Recognizing Speech:
AI can help people learn languages by giving them information, tasks, and feedback on how they say words. Speech recognition technology lets kids talk and listen to each other in a fun and dynamic way. AI language learning tools can help people learn languages and become more aware of other cultures from a young age.
5. Getting a Head Start on Learning Problems:
AI programs can look at how children learn and how they act to see if they might have learning problems or be behind in their growth. When these problems are found early, they can be fixed and helped right away, giving children the help they need to beat them and do well in school and in their social lives.
6. SEL Stands for "Social and Emotional Learning:"
Educational tools that are driven by AI can help people learn about social and emotional skills by simulating how people connect with each other and show how they feel. Children can learn kindness, self-awareness, and social skills in a safe and controlled setting by playing roles and sharing stories in virtual worlds.
7. Data-Driven Parent Engagement:
AI makes it easier for teachers and parents to talk to each other by giving them data-driven information about how a child is learning and growing. AI-powered platforms for parent involvement can make personalized ideas for learning tasks that can be done at home and ways to help a child along his or her educational path.
But there are some important things to think about and possible problems with putting AI into early childhood education 6-9,31:
1. Data Privacy and Security: In school settings, it is very important to keep children's information safe. Schools and educational sites need to make sure that AI-powered tools follow data protection rules and keep high data security standards.
2. Ethical Use of AI in Education: AI should be built and used with ethics in mind, making sure that its methods don't promote biases or favor certain groups of learners over others.
3. Finding a balance between AI and human interaction: AI can make learning more fun, but it shouldn't replace human teachers. For young learners to build real connections and get personalized help, it's important to keep a balance between AI-powered tools and human contact.
4. Interfaces that are easy to use: AI-powered tools for early childhood education must be made with ease of use in mind. Interfaces should be easy to use and proper for the age of the user, so that even young learners can use the tools on their own. In conclusion, AI in early childhood education could change how young children learn and grow in a big way. AI can help set up a strong base for ongoing learning by giving people personalized learning experiences, helping them learn a language, promoting social and emotional development, and making early intervention easier. But it's important to think about ethics and data privacy and make sure there's a balanced method that blends AI with human connections to make the best learning setting for young people.

Insights and Applications of AI-Driven Behavior Analysis in Child Psychology:

Behavior analysis is an important part of child psychology because it tells us a lot about a child's mental, emotional, and social growth. Traditionally, behavioral analysis has been based on direct observation and handwritten judgments, which can be time-consuming and not always accurate. But now that artificial intelligence (AI) has come a long way, researchers and practitioners are looking into AI-driven behavioral analysis as a powerful way to learn more about children's actions and make objective observations of them. This piece talks about what AI-driven behavioral research can teach us about children and how it can be used 10-14,32
1. Data Collection and Analysis That Are Done Automatically:
AI-driven behavioral analysis uses video records, wearable devices, and engaging platforms, among other data sources, to collect data about behavior without being intrusive. Then, AI systecan look at these streams of data to find patterns, trends, and connections in how a child acts over time. Researchers can collect and analyze a lot of behavioral data quickly and easily thanks to automated data gathering and analysis. This lets them make more complete and detailed assessments.
2. Objective Measurements of Behavior:
AI-driven behavioral analysis aims to measure children's actions in a way that is not influenced by human bias or opinion. Using algorithms and machine learning models, AI can correctly spot behavioral signs like facial expressions, gestures, and verbal cues. This lets researchers evaluate a child's mental state, social interactions, and cognitive responses in a fair way.
3. Identifying Developmental Challenges Early:
One of the most important uses of AI-driven behavioral analysis is to find developmental problems and behavioral problems in children as early as possible. By looking at behavioral data, AI systems can find small changes from normal development paths, which could be signs of developmental delays or odd behavior. When children are identified early, they can get help and support right away, which is good for their general growth.
4. Strategies for Personalized Intervention:
AI-driven study of a child's behavior can help make personalized intervention plans that fit the child's wants and challenges. By looking at a child's behavior data, AI can figure out what the best and most effective interventions are, making sure that they are focused and fit the child's strengths and flaws.
5. Understanding How to Interact with Other People and How to Talk to Them:
AI-driven study of how people connect with each other and how well they can communicate is especially useful in child psychology. AI can figure out how a kid interacts with others, how they talk to each other, and how they respond to social cues by looking at video recordings or digital exchanges. This helps experts and therapists learn more about how a child builds relationships and communicates.
6. Keeping Track of Progress and Treatment Results:
Behavior analysis that is driven by AI makes it possible to keep track of a child's growth during interventions or treatments. By comparing facts about a person's behavior before and after an intervention, researchers can get a clear picture of how the treatment worked and how well it worked over time.
7. Research on Neurodevelopment:
AI-driven behavioral analysis is used in the area of neurodevelopmental science to study the links between the brain and behavior. By mixing AI-driven behavioral measures with neuroimaging data, researchers can learn more about the brain processes behind different behaviors. This gives them a more complete picture of how children grow and develop.
Even though AI-driven behavioral analysis has some interesting uses, there are some problems that need to be solved 15-18,33:
1. Ethical Considerations: When using AI-driven behavioral analysis with children, it's important to think about things like data safety, informed agreement, and the right way to use behavioral data.
2. Interpreting AI-Driven Results: It may be hard for non-technical workers to understand and make sense of the results that AI programs produce. Researchers and practitioners need to work well together to make sure that AI results are used to improve methods that are based on evidence.
In conclusion, AI-driven behavioral analysis has a lot of promise to help us learn more about how children behave, how they grow, and how their mental health is doing. AI helps advance the field of child psychology and improve the well-being of children by automating data collection, giving objective assessments, and supporting personalized treatments. As researchers and practitioners keep looking into AI's uses, it's important to think about social issues and work together well to get the most out of AI-driven ideas for child psychology.

Ethical Things Using AI to Assess the Mental Health of a Child:

Using AI to evaluate a child's mental health has many benefits, such as spotting mental health problems early and giving each kid a unique treatment plan. But it also brings up important ethical questions that must be carefully thought through if AI is to be used in this sensitive area in a responsible and ethical way. Some of the most important social concerns are 19-22, 34:
1. Privacy and Data Security: There are strict rules about privacy and data security that must be followed when collecting and studying behavioral and psychological data from children. To keep the child's information private and stop illegal access, it is important to make sure that data is anonymized, protected, and kept safely.
2. Before using AI to evaluate a child's mental health, it is important to get permission from the child's parents or legal guardian. This is called "informed consent." Parents should know everything there is to know about the goal of the test, how data will be collected, how the data will be used, and what risks and benefits might be involved.
3. Transparency and Explainability: AI systems that are used to evaluate mental health should be clear and easy to understand. To make sure that the AI's review is correct, fair, and unbiased, it is important to be able to understand how it came to its findings.
4. Avoiding Bias and Discrimination: AI systems can unintentionally repeat the flaws in the data they were trained on. Be careful to get rid of any possible bias based on race, gender, culture, or socioeconomic position. To find and fix any flaws that may come up as the AI is used, it needs to be audited and monitored on a regular basis.
5. Sensitive Use of Data: Data about a child's behavior and mental health is very sensitive and can have long-term effects on their health. It is very important to use this information in a responsible way and only to help people with their mental health.
6. Human Oversight and Intervention: AI can help measure mental health, but it shouldn't be used instead of people who are trained in mental health. Human control is needed to figure out what AI results mean, put them in context, and make choices about a child's mental health that are well-informed.
7. Avoiding Stigmatization: AI-based mental health exams should be done with care so that children who may already be having mental health problems don't get a bad name. Carefully and in a helpful way, the results should be shared.
8. Continued Research and Validation: AI systems used to evaluate a child's mental health should go through thorough testing, validation, and ongoing research to make sure they are accurate and useful.
9. Responsible Marketing and Advertising: Companies and groups that use AI to evaluate children's mental health must use responsible marketing methods and not make false promises about the accuracy or usefulness of their AI systems.
10. Compliance with rules and regulations: AI used to evaluate a child's mental health must follow the rules and guidelines set by healthcare officials and governing bodies.
Overall, using AI to evaluate a child's mental health has a lot of potential, but it needs to be done carefully with an eye toward ethics. By putting privacy, informed consent, openness, fairness, and human control at the top of the list, we can use AI to help children with their mental health while protecting their rights and well-being.

Interventions with the Help of AI for Kids with Developmental Disorders:

Children with developmental problems like autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and language impairments are getting more and more help from AI-assisted treatments. These interventions use artificial intelligence to give personalized and focused help to children with developmental challenges. This helps them make progress in their growth and improves their general quality of life. Here are some of the most important things about AI-assisted treatments for kids with developmental problems and how they can be used 23-27,35:
1. Early Detection and Diagnosis: AI programs can look at different types of data, such as studies of behavior, medical records, and regular tests, to help find and diagnose developmental disorders early on. Early detection makes it possible to act quickly, which is important for better growth.
2. AI-powered tools can figure out a child's skills, weaknesses, and preferred ways to learn so that educational material and therapeutic approaches can be made to fit those needs. Personalized learning plans help kids get involved in tasks that match their skills, which helps them learn and stays motivated.
3. Social skills training: AI-powered social skills training programs give kids the chance to practice interacting with others in a safe and controlled setting. Children can use virtual figures or images to act out real-life situations and improve their social skills.
4. Language Development and Communication: AI-based language development tools can help children who have trouble speaking or speak slowly by giving them targeted language activities, helping them learn new words, and giving them feedback on how they say words.
5. Attention and Focus: For kids who have trouble paying attention, AI-powered tools can change learning materials to keep them interested and on task. Adaptive methods may be used in these tools to change the level of difficulty of jobs based on how well the child does.
6. Emotion Recognition and Regulation: AI can be used to read children with developmental illnesses' facial movements and emotional cues. AI can help children better understand and control their feelings by giving them feedback and techniques in real time.
7. Assistive communication devices are powered by AI and can help non-verbal children who have trouble communicating by turning their actions, pictures, or eye movements into important messages.
8. Data-Driven growth Monitoring: AI algorithms can keep track of a child's growth throughout the intervention, giving teachers, therapists, and parents important information about how well the chosen methods are working. Monitoring success based on data lets changes be made quickly to improve the results of an action.
9. Home-Based help: AI-assisted interventions can go beyond clinical settings to offer ongoing help and learning chances in the child's home surroundings. Virtual learning tools and AI-powered mobile apps make it easy for parents and guardians to find useful information.
10. AI-assisted interventions give parents, caregivers, and teachers the tools, advice, and knowledge they need to help children with developmental disorders in the best way possible.
Even though AI-assisted treatments for children with developmental disorders have many benefits, there are some social questions that need to be answered. These include making sure that data is kept private, that AI algorithms are used in a transparent way, that flaws in AI systems are fixed, and that human workers are still an important part of the intervention process so they can offer emotional support and context.
In conclusion, AI-assisted treatments show a lot of promise for helping children with developmental disorders by offering personalized learning, training in social skills, language development, and more. These measures could make a big difference in how children grow and develop and improve the quality of life for them and their families. As AI technologies continue to improve, it is important to find a balance between the benefits of AI and social concerns to make sure that these tools are used in a responsible and effective way to help children.

AI impact on Children's Social and Emotional Skills Growth:

AI is a key part of knowing how children grow socially and emotionally because it helps us understand their feelings, how they interact with others, and how they act. AI algorithms can find patterns, trends, and correlations in a lot of data from many different sources. This helps us learn more about how children's social and mental growth works. Here are some important ways that AI helps us understand this 28-31,36:
1. Emotion Recognition and Analysis: AI-powered systems can read and understand children's facial expressions, voice intonations, and body language to figure out how they are feeling. Researchers and teachers can use this to see how children show and understand their feelings, which gives them information about their emotional growth.
2. Social Interaction Analysis: AI can look at how children interact with each other in different places, like schools, parks, or virtual worlds. AI can learn a lot about children's social behavior and ties with their peers by watching how they talk and interact with each other.
3. Identifying Developmental Milestones: AI algorithms can use data from regular tests, notes, and developmental milestones to track a child's growth in social and emotional development. This helps find problems or delays in growth as early as possible.
4. Personalized Social and Emotional Learning: AI can make social and emotional learning tools based on each person's skills and weaknesses. Personalized learning makes sure that children get help that fits their special needs as they grow.
5. Detecting Social and Emotional Problems: AI can help find signs of social and emotional problems in children, such as social nervousness, problems with controlling emotions, or problems with social communication. When a problem is found early, help and support can be given right away.
6. AI makes it possible to keep an eye on how children's social and mental growth changes over time. By looking at data from different places in time, teachers and therapists can track growth and figure out how well interventions are working.
7. AI-powered virtual settings can give kids a safe place to practice social skills and learn how to handle real-life situations. This is called "virtual social skills training." Children can use virtual social skills training to learn, make mistakes, and get feedback in a safe setting.
8. Supporting Emotional Well-Being: AI can give children who are having emotional problems real-time social help and ways to deal with their feelings. Chatbots or virtual helpers can help kids deal with their feelings by giving them age-appropriate tools and tips.
9. Ethical Considerations: AI technologies need to think about things like data protection, getting educated agreement, and avoiding biases. Making sure AI systems are clear and easy to understand is important for building trust and making sure they are used in a responsible way.
10. Improving study and Knowledge: AI-powered data analysis lets researchers process and understand huge amounts of data, which speeds up study on social and mental development. This information helps to improve methods and solutions that are based on facts.
AI's ability to handle and analyze big data sets gives us a unique chance to learn more about how children grow socially and emotionally. But it is important to blend ideas from AI with human knowledge and empathy. AI and experts work together to make sure that children's emotional health and developmental needs stay at the top of study and interventions. By using AI's ability in a responsible way, we can help kids build healthy social and emotional skills, which will be good for them in the long run.

Helping Parents with AI:

AI-powered parenting support has a lot of promise to help parents with different parts of raising children, from when they are young to when they are teenagers. It can give personalized tips, interventions at the right time, and methods based on research to help parents do good things for their kids and help them grow and learn. But along with these chances, there are also a number of problems that need to be carefully dealt with. Here are some of the problems and benefits of using AI to help with parenting 2,30,37:
Challenges:
1. Data Privacy and Security: For AI-powered parenting help to work, personal information about children and their family’s needs to be collected. To protect families' safety and well-being, it is important to keep data private and safe.
2. Ethical Use of Data: When AI is used to help parents, it is important to use data in an ethical way. Transparency, informed permission, and avoiding bias are all important things to think about when using data.
3. Reliability and Accuracy: For AI programs to give good tips and suggestions, they need to be accurate and reliable. AI models need to go through a lot of testing and validation to make sure they are reliable.
4. Complex Human feelings: It's hard for AI to understand and interpret complex human feelings, especially in young children. It may be hard for AI systems to fully understand empathy and emotional details.
5. Bias Elimination: Unintentionally, AI programs may repeat biases that were in the data used to teach them. To make sure everyone gets fair and equal help, efforts must be made to find and get rid of flaws.
6. Overreliance on AI: AI-enabled help with parenting should not replace human judgment and experience, but rather add to them. If you rely too much on AI, you might forget how important human contact and mental support are.
7. Cultural and Contextual Sensitivity: AI programs should be made with cultural and contextual sensitivity in mind so that they can adapt to different ways of parenting and views.
Opportunities:
1. Personalized Parenting Advice: AI can give advice and methods that are unique to a family's needs and problems, making it possible for different parenting styles to get the help they need.
2. Interventions at the right time: AI can spot early signs of developmental problems or parenting stress and help parents deal with problems before they happen.
3. Parenting skill improvement: AI can make it easier for parents to find evidence-based parenting tools, classes, and interactive modules, which can help them get better at parenting.
4. AI-enabled parenting support gives parents the power to make decisions about their child's growth because it gives them the knowledge and courage to do so.
5. Creating a sense of community and support among caregivers: AI can connect parents who are going through similar problems.
6. Longitudinal Tracking: AI can keep an eye on how a child grows and changes over time. This makes it easier to track growth over time and find patterns.
7. Access to Remote Areas: AI can help parents in remote or impoverished areas get the help they need, making helpful tools easier to get to.
8. Reducing Stigma: AI-driven support may make it less embarrassing to ask for help with parenting or mental health, making it easier to reach out for help.
In conclusion, using AI to help with parenting presents both problems and chances. To use AI to help parents in the best way possible, it is important to address data protection, ethical issues, bias reduction, and human-AI collaboration. By finding a good mix between technology progress and human connection, AI can become a useful tool for encouraging good parenting and promoting healthy child development.

Conclusion:

The use of artificial intelligence (AI) in parenting support has opened up new ways to help parents with different parts of raising children. For example, parents can now get personalized tips, methods based on evidence, and timely solutions. The study of how AI and parenting work together has shown both the possibilities and difficulties that this growing field offers. The use of AI to help parents is a hopeful way to improve parenting and help kids grow and learn. AI can help prevent developmental problems and parenting stress by giving personalized advice based on the needs of each family and offering quick solutions. Access to parenting tools and classes based on facts gives parents the knowledge and confidence they need to make choices for their child's well-being that are based on facts. Also, AI-driven support can build a sense of community, making it less embarrassing to ask for help with parenting or mental health.
But it's important to think about ethics when using AI to help parents in a responsible way. To protect the safety and well-being of families, it is very important to make sure that data is safe, secure, and used in a good way. To give accurate and trustworthy advice, AI systems need to be tested and validated thoroughly. It's important to deal with biases if everyone's families are to get fair and equal help. AI should not replace human knowledge and mental connections when it comes to parenting. The human touch is still the best way to give children and parents mental support and understanding. It is important to be sensitive to culture and context in order to accommodate different parenting styles and views and create a welcoming and supportive atmosphere. In conclusion, the combination of AI and family support has a lot of potential to give parents more power and help children grow up well. By figuring out how to handle the problems and take advantage of the possibilities in an ethical way, AI can become a helpful partner for parents on their parenting journey. By finding a good mix between technology progress and human connection, we can use AI to its fullest extent to help children be as healthy as possible and encourage good parenting practices in the digital age. By using AI as a helpful tool along with human knowledge, we can make sure that children and families get the care, understanding, and help they need to do well.

References

  1. Agrawal, S.K.; Chen, X.; Ragonesi, C.; Galloway, J.C. (2012). Training toddlers seated on mobile robots to steer using force-feedback joystick. IEEE Transactions on Haptics, 5(4), 376–383. [CrossRef]
  2. Arksey, H.; O’Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32. [CrossRef]
  3. Bartlett, B.; Estivill-Castro, V.; Seymon, S.; Tourky, A. (2003). Robots for pre- orientation and interaction of toddlers and preschoolers who are blind. In Proceedings of the australasian conference on robotics and automation 13-23. Australian Robotics and Automation Association (ARAA).
  4. Berson, I.R.; Luo, W.; Yang, W. (2021). Narrowing the digital divide in early childhood: Technological advances and curriculum reforms. Early Education and Development. [CrossRef]
  5. Burgsteiner, H.; Kandlhofer, M.; Steinbauer, G. (2016, March). Irobot: Teaching the basics of artificial intelligence in high schools. In Proceedings of the AAAI conference on artificial intelligence, 30 pp. 4126–4127. [CrossRef]
  6. Ca˜namero, L.; Lewis, M. (2016). Making new “new AI” friends: Designing a social robot for diabetic children from an embodied AI perspective. International Journal of Social Robotics, 8(4), 523–537. [CrossRef]
  7. Chassignol, M.; Khoroshavin, A.; Klimova, A.; Bilyatdinova, A. (2018). Artificial intelligence trends in education: A narrative overview. Procedia Computer Science, 136, 16–24. [CrossRef]
  8. Chen, X.; Xie, H.; Zou, D.; Hwang, G.J. (2020). Application and theory gaps during the rise of artificial intelligence in education. Computers in Education: Artificial Intelligence, 1, 100002. [CrossRef]
  9. Chen, X.; Zou, D.; Xie, H.; Cheng, G.; Liu, C. (2022). Two decades of artificial intelligence in education: Contributors, collaborations, research topics, challenges, and future directions. Educational Technology & Society, 25(1), 28–47.
  10. Druga, S.; Ko, A.J. (2021). How do children’s perceptions of machine change when training and coding smart programs?. In Interaction design and children (pp. 49–61).
  11. Druga, S.; Vu, S.; Likhith, E.; Qiu, T. (2019). Inclusive AI literacy for kids around the world. Proceedings of the FabLearn, 104–111. [CrossRef]
  12. Dwivedi, U. (2021). Introducing children to machine learning through machine teaching. In Proceedings of the IDC’ 21 interaction design and children (pp. 641–643). [CrossRef]
  13. Fomichov, V.A.; Fomichova, O.S. (1995). The artificial intelligence theory and highly effective methods of teaching young children foreign languages. Cybernetica, 38(4), 321–344.
  14. Ge, D.; Wang, X.; Liu, J. (2021). A teaching quality evaluation model for preschool teachers based on deep learning. International Journal of Emerging Technologies in Learning, 16(3), 127–143. [CrossRef]
  15. Giullian, N.; Ricks, D.; Atherton, A.; Colton, M.; Goodrich, M.; Brinton, B. (2010, October). Detailed requirements for robots in autism therapy. In 2010 IEEE international conference on systems, man and cybernetics. Turkey: Istanbul.
  16. Goksel, N.; Bozkurt, A. (2019). Artificial intelligence in education: Current insights and future perspectives. In Handbook of research on learning in the age of transhumanism (pp. 224–236). IGI Global.
  17. Herodotou, C. (2018). Young children and tablets: A systematic review of effects on learning and development. Journal of Computer Assisted Learning, 34(1), 1–9. [CrossRef]
  18. Jin, L. (2019, May 17-18). study on influences of artificial intelligence era on early childhood family education in China. In The 4th annual international conference on information system and artificial intelligence, hunan, China. [CrossRef]
  19. Jin, L. (2019a). Investigation on potential application of artificial intelligence in preschool children’s education. In The 5th annual international conference on network and information systems for computers (ICNISC2019). Wuhan, China. [CrossRef]
  20. Kilian, M.; Chapple, I.L.; Hannig, M.; Marsh, P.D.; Meuric, V.; Pedersen, A.M.; et al. (2016). The oral microbiome – An update for oral healthcare professionals. British Dental Journal, 221, 657-666. [CrossRef]
  21. Scotti, E.; Boue, S.; Sasso, G.L.; Zanetti, F.; Belcastro, V.; Poussin, C.; et al. (2017). Exploring the microbiome in health and disease: Implications for toxicology. Toxicology Research and Application, 1, 1-37. [CrossRef]
  22. Gao, L.; Xu, T.; Huang, G.; Jiang, S.; Gu, Y.; Chen, F.; et al. (2018). Oral microbiomes: More and more importance in oral cavity and whole body. Protein & Cell, 9, 488-500. [CrossRef]
  23. Yamashita, Y.; Takeshita, T. (2017). The oral microbiome and human health. Journal of Oral Science, 59, 201-206. [CrossRef]
  24. Lane, N. (2015). The unseen world: Reflections on Leeuwenhoek (1677) ‘concerning little animals’. Philosophical Transactions of the Royal Society B: Biological Sciences, 370, 20140344. [CrossRef]
  25. Malla, M.A.; Dubey, A.; Kumar, A.; Patil, A.; Ahmad, S.; Kothari, R.; Yadav, S. (2021). Using RSM and GC-MS-based metabolomics, the organophosphorus and pyrethroid degradation pathways of a new bacterial group called C3 were figured out and made better. The Taiwan Institute of Chemical Engineers publishes this journal, 144, 104744.
  26. Munot, N.; Kandekar, U.; Rikame, C.; Patil, A.; Sengupta, P.; Urooj, S.; Bilal, A. (2022). A systematic study called Improved Mucoadhesion, Permeation, and In Vitro Anticancer Potential of Synthesised Thiolated Acacia and Karaya Gum Combination. Molecules, 27(20), 6829.
  27. Munot, N.; Kandekar, U.; Giram, P.S.; Khot, K.; Patil, A.; Cavalu, S. (2022). A Study of Quercetin-Loaded Nanocochleates and Liposomes: Formulation, Characterization, Assessment of Degradation, and In Vitro Anticancer Potential. Pharmaceutics, 14(8), 1601.
  28. Manikyam, H.K.; Tripathi, P.; Patil, S.B.; Lamichhane, J.; Chaitanya, M.; et al. (2020). Hesperidin from the unripe Citrus grandis/maxima fruit Nepal variety was taken out, cleaned up, and measured. Asian Journal of Biochemistry of Natural Products, 20(1).
  29. Patil, A.; Munot, N.; Patwekar, M.; Patwekar, F.; Ahmad, I.; Alraey, Y.; Alghamdi, S.; et al. (2022). Encapsulation of lactic acid bacteria by lyophilization and its effects on the bacteria's ability to live and stick together. Complementary and alternative medicine based on evidence, 2022.
  30. Nalawade, A.S.; Gurav, R.V.; Patil, A.R.; Patwekar, M.; Patwekar, F. (2022). A full review of the studies of the genus Chlorophytum Ker Gawl's morphological, genetic, and phytochemical variety, breeding, and bioprospecting. made in India. Trends in the study of phytochemicals, 6(1), 19-45.
  31. Patil, K.G.; Balkundhi, S.; Joshi, H.; Ghewade, G. (2011). International Journal of Pharmacy and Pharmaceutical Research: MEHSANA BUFFALO MILK AS PREBIOTICS FOR GROWTH OF LACTOBACILLUS, 1(1), 114-117.
  32. Das, N.; Ray AR Patil SS Saini BWaghmode CGhosh, P.B. Patil, and others. By causing apoptosis and going after the -catenin/Wnt pathway, chosen Indian honey stops the growth of colon cancer cells. Food and work, 13(15), 8283-8303.
  33. Patil, M.J.; Mali, V. (2021). Different Tests of the Cytotoxicity of Lactobacillus Found in Sheep Milk. Acta Scientific Pharmaceutical Sciences, 5(12), 69-70.
  34. Abhinandan PS, P.; John, D. (2020). With its ability to stick to cells, Lactobacillus plantarum has probiotic promise. Global Pharma Technology Journal, 10(12), 1-6.
  35. Patil, A.; Mali, V.; Patil, R. (2019). A 6-month-old baby's banana fibres looked like a gut worm. Iberoamerican Medical Journal, 2(3), 245-247.
  36. Munot, N.M.; Shinde, Y.D.; Shah, P.; Patil, A.; Patil, S.B.; Bhinge, S.D. (2021). Formulation and Evaluation of Chitosan-PLGA Biocomposite Scaffolds Incorporated with Quercetin Liposomes Made by QbD Approach for Better Healing of Oral Lesions. PharmSciTech, AAPS, 24(6), 147. [CrossRef]
  37. Patil, A. (2023). Psychology in a time when people are dependent on technology and the Mobile Dilemma. Preprints.org, 2023070101.
  38. Patil, A.; Kotekar, D.; Chavan, G. (2023). How probiotics affect the development and spread of cancer and how to figure out how they work. Preprints.org, 2023070243.
  39. Pawar, V.; Patil, A.; Tamboli, F.; Gaikwad, D.; Mali, D.; Shinde, A. (2021). A complete look at how AI can be used in pharmacokinetics and pharmacodynamics. PharmSciTech, AAPS, 14(2), 426-439.
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