Abstract
Artificial Intelligence (AI) has emerged as a valuable tool in seamlessly integrating three important components of talent development: academic acceleration, classroom inductive teaching techniques focusing on depth and complexity, and opportunities for interest-based activities. These three components of talent development within gifted education services can be likened to a three-legged stool. Each component is crucial to accommodate and stimulate the intellectual growth of gifted students.
Keywords
Artificial Intelligence is significantly transforming the landscape of education and talent development by making learning more personalized.”
Everyone knows that one-size fits all doesn’t work well with respect to shoes or clothing. It also doesn’t work well in education. Students enter school with different experiences and learn at different rates. Not everyone is the same. However, just because a child learns more slowly or more quickly than their peers, it doesn’t mean they should sacrifice their right to learn something new.
Research shows that a typical fifth grade classroom might have a five- to seven-grade academic achievement range (Peters et al., 2017; Rambo-Hernandez et al., 2020). We also know from research at the National Center for Research on Gifted Education that when teachers are given time to differentiate content and instruction, they tend to focus exclusively on struggling learners (Kenny et al., 2024). Meeting the varying student needs in classrooms is difficult; however, technology can help ensure that every student has an opportunity to learn something new every day and this includes students who have advanced learning needs.
The talent development components of gifted education services that generally address students’ advanced learning needs can be thought of as legs on a three-legged stool (Siegle, 2022). Each of the three legs is necessary for the stool to function. Similarly, each of three key talent development components is necessary to meet the diverse learning needs of gifted students. These three components are (1) academic acceleration options; (2) inductive teaching classroom instruction that involves depth and complexity; and (3) opportunities to explore interest-based activities (see Figure 1). Technology can assist with each of these. Three legs of talent development.
First Leg of the Talent Development Stool: Acceleration
Some students simply outpace their grade-level curriculum requirements. For those who have fully mastered their grade-level curriculum, whole-grade acceleration is often warranted. According to the National Center for Research on Gifted Education (NCRGE, 2024), students who are achieving two grade levels ahead in reading and mathematics are possible candidates for whole-grade acceleration. This would be a second-grade student who is scoring minimally at the 50th percentile nationally on fourth grade reading and fourth grade mathematics achievement tests. Students considered for whole-grade acceleration also tend to be in the top 10% on cognitive tests. Depending on the school size, one or two students in a given grade might be good candidates for whole-grade acceleration, although this can vary. Additionally, about 10% of students are candidates for mathematics or reading/language arts subject acceleration. The decision whether to grade skip a student is an important one and a concern for most parents and educators. Fortunately, newly released technology driven systems can help educators and parents make informed decisions whether students are good candidates for acceleration.
The University of Iowa is well-known for its research in the area of academic acceleration. On the basis of over three decades of research, Iowa recently released the Integrated Acceleration System (https://accelerationsystem.org/), an online platform designed to assist parents and educators in making decisions about acceleration. The online platform systematically accounts for the important factors that should be considered when making an acceleration decision by applying algorithms to data provided by parents, teachers, and the student. The Integrated Acceleration System guides stakeholders through a discussion about the students’ strengths and generates a report detailing whether acceleration would be a good fit for a particular student. The current version covers whole-grade acceleration, although future versions will also focus on subject-specific acceleration, early entrance into kindergarten, and early entrance into college.
Not every gifted student is a candidate for whole-grade or subject-specific acceleration. Some students simply need parts of their curriculum compacted (Reis et al., 2016). Administering a pre-assessment, such as a pretest, is an easy strategy to determine which standards students have already mastered. Pre-assessment is one of the most overlooked tools in the gifted education arsenal. A good rule of thumb is to consider students for curriculum compacting if they demonstrate at least 80%–85% mastery of the content being covered. With this level of mastery, educators eliminate the mastered content from the student’s classroom activities and provide them with material that is better suited to their advanced learning needs.
The three legs of talent development are not mutually exclusive. The replacement activity for students who require having their curriculum compacted might involve content with greater depth and complexity, the second leg of talent development, or an opportunity to pursue an interest-based activity, the third leg of talent development.
Regardless of the replacement activity, curriculum compacting assures students are learning something new every day, and technology can assist in finding replacement activities. In addition to AI-driven lesson planning sites such as https://magicschool.ai, https://curipod.com, and https://classcompanion.com, the Khan Academy has embraced artificial intelligence (AI) with Khanmigo (https://khanmigo.ai/). Khanmigo is a safe and accurate differentiating tutor, thanks to the Khan Academy group training it on their material while building it atop ChatGPT. Khanmigo is an excellent resource for several reasons. First, it does not give the answer, rather it guides students’ thinking. Second, it provides a judgment free environment for learning. This is important for shy students. Third, it adjusts to the students’ learning level. Although this is important for all students, it is particularly beneficial for increasing learning outcomes for struggling students and advanced students, who often hit an instructional ceiling. Finally, it keeps transcripts of every student interaction so teachers can monitor and evaluate student progress (Tyrangiel, 2024).
Teachers usually find themselves teaching to the middle of the class. However, Khanmigo will guide students who have mastered the content to explore further concepts. Although we have only scratched the surface of AI potential in education, Khanmigo is differentiating instruction like never before, and it is putting educators at the doorstep of personalized learning.
Second Leg of the Talent Development Stool: Inductive Teaching with Depth and Complexity
Gifted students spend most of their time in a traditional classroom setting, and the learning environment they encounter in that classroom can dramatically impact their motivation and academic achievement. Grade skipping a highly talented student from one uninspiring classroom to a higher-grade uninspiring classroom is not necessarily the most productive talent development path. Therefore, the second leg of talent development requires that conventional classrooms promote depth and complexity of thinking (higher levels of Webb’s Depth of Knowledge and Kaplan’s Depth and Complexity Icons) and inductive learning. This not only benefits the gifted students in the class, but it also benefits all of the students in the class and can be a vehicle for identifying students whose talents were overlooked.
Gifted students thrive in inductive learning environments where teaching starts with specific examples or observations and moves towards generalizations or theories. In this environment, students are encouraged to derive general principles or concepts through guided inquiry. They are actively engaged in the learning process as they analyze material, identify patterns, and draw conclusions based on evidence. Because gifted students typically enjoy challenges that stimulate their intellect, the inductive approach is popular. It actively engages their analytical and critical thinking skills. It also provides them with ownership of the learning as they formulate their conclusions based on evidence and reasoning. Students can interact with AI chatbots such as https://chat.openai.com as they employ their creativity and think outside the box to explore multiple perspectives and potential solutions.
Gifted students often crave a deeper understanding of topics beyond surface-level knowledge, and the inductive approach facilitates this depth of understanding by encouraging inquiry and exploration. In Kaplan’s depth and complexity approach, depth refers to a deep understanding of the content within a field. This involves investigating language, details, patterns, rules, trends, unanswered questions, ethics, and big ideas related to the topic. Complexity, on the other hand, involves scholarly insights into connections across time, people, and disciplines. It includes exploring how a topic has changed over time, considering different perspectives on the subject matter, and understanding how it relates to other disciplines (Kaplan, 2009; TeachThought, 2020).
Technology can be helpful in both the arena of inductive learning and greater depth and complexity. For example, students can use OpenAI’s ChatGPT (https://chat.openai.com/) and Perplexity (https://perplexity.ai/) to delve deeper into content or explore relationships across discipline through interactive conversations with the bots.
Perplexity is a new AI-driven search engine that has surfaced to challenge Google’s Gemini (formerly Bard) and Microsoft’s Bing. Perplexity.ai (also available as an app) offers several advantages. The free service collects data within websites and provides a summary of information across websites with links to the specific URLs or domains where it found the data. Perplexity AI combines search engine intelligence with chatbot capabilities, offering users the best of both worlds by answering questions in real-time and explaining search results with references. It also provides suggested follow-up questions. Additionally, Perplexity AI is designed to provide targeted results, making it an ideal tool for research. For example, it can be asked to only report results from a given set of sources, such as only searching peer-reviewed journal articles. The paid version also can generate text output from any text data the user uploads.
Perhaps a student wishes to explore the relationship between climate and economics. Figure 2 shows Perplexity’s response to a prompt about the positive and negative economic impact of global warming. In addition to providing a summary of the topic, it lists the sources for the summary and suggests related questions to explore. The student can follow up with one of the suggested questions or formulate their own as they explore the relationship in greater depth and complexity. Example using Perplexity.ai to extend depth and complexity.
Third Leg of the Talent Development Stool: Interest-Based Activities
Today’s students live in a world filled with choices based on their interests. They can customize the food selection at their favorite sandwich, pizza, or ice cream shop. They have 24/7 unlimited music and video selections. However, they seldom have choices in school and often are not provided with opportunities or time to explore their interests.
The importance of addressing students’ interests and providing choices to make learning more meaningful is indisputable. Most gifted students are passionate about topics that interest them, and educators and parents should never underestimate the role interest and choice play in achievement. Interest is one of the strongest self-reported predictors of achievement across a wide variety of domains. There is a positive relationship between interest and high performance (Siegle et al., 2010), and students who are given choices about their learning are more motivated (Merrill & Gonser, 2021). Therefore, given the importance of student interest and choice, the third essential component of talent development is providing students with learning opportunities related to their interests.
Renzulli and Reis (2014) have long been proponents of providing talented students with opportunities to apply their interests to create authentic products or services for authentic audiences through Type III enrichment experiences. Type III independent and small group projects are the key gifted component of their Schoolwide Enrichment Model. To facilitate interest-based learning, they developed a fee-based online platform (https://renzullilearning.com) that assesses students’ interests and favored learning and expression preferences and provides students with activities related to their interests and preferences.
Beghetto (2017) suggests students should participate in Legacy Projects, which are similar to Schoolwide Enrichment Type IIIs. Legacy Projects are “student-directed, creative endeavors aimed at making a lasting contribution by addressing complex challenges in students’ lives, schools, communities, and beyond … that require students to tackle real-world problems” (p. 187). Beghetto has developed an AI bot built on OpenAI’s ChatGPT 4 (chat.openai.com/g/g-OKa52dVlF-legacy-project-bot) that guides students through his four step Legacy Project process of (1) What is the problem? (2) Why does it matter? (3) What are we going to do about it? and (4) What lasting legacy will our work leave as a result of addressing this problem? The bot is designed to expand students’ thinking about their projects by asking them questions to be more reflective.
Often students lack the necessary executive functioning skills to successfully execute an interest-based project. Goblin.tools is a free online platform that uses OpenAI’s model to organize tasks. It was designed to assist neurodivergent individuals with tasks that they might find overwhelming. In addition to the fun options such as Chef that creates a recipe for ingredients that users enter, The Judge that assists users in determining the tone of a message (see Figure 3), and Formalizer that changes the style of text it is provided, Goblin.tool has several features that students will find helpful in planning and organizing their interest-based projects. Magic ToDo creates a list of steps to complete a task. Estimator estimates a timeframe for completing a task. Compiler provides a list of to-do items from a narrated text. Goblin.tools’ The Judge infers tone from written text.
Figure 4 illustrates a Magic ToDo list for editing a video. Students can adjust how much breaking down of the task they need by selecting the number of chili peppers at the top. By clicking on what appears as a diagonal blue magic wand to the right of an item, the system will automatically generate a list of to-do items. This includes the option to expand a to-do item into subtasks. The vertical ellipses to the right of the wand produces an additional menu to edit a given to-do item. Sample of a magic ToDo list of steps to edit a video.
When students wish to create images for their interest-based work, they can turn to one of the many AI texts to image generators that have surfaced over the past year. Sophisticated photo editing programs such as Adobe’s Photoshop now employ image generation options (adobe.com/products/firefly.html). Google’s Slides provides a free option to create images from text. Users simply select Create image with Gemini from the Insert → Image menu and describe the image they wish to create in the dialogue box that appears. (see Figure 5). They have a choice of styles ranging from vector art to watercolor to photography. Using text to image to insert an image into Google Slides.
Artificial Intelligence is significantly transforming the landscape of education and talent development by making learning more personalized. While AI brings numerous advantages, it also poses challenges such as data privacy concerns, the digital divide, and the need for educators to adapt to new technologies. AI tools can hallucinate and spit out false information and magnify racial and socioeconomic biases (Herold, 2022). There is ongoing debate about what school policies should be in place to ensure these technologies are used ethically and equitably to benefit all learners (Klein, 2024). However, as these technologies continue to evolve, they hold the promise of further enhancing the educational landscape by assisting educators and parents in developing students’ talents and preparing them for the challenges of the future.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Bio
Del Siegle, PhD, is the Lynn and Ray Neag Endowed Chair for Talent Development at the University of Connecticut, where he directs the National Center for Research on Gifted Education. He is a past-president of NAGC and recipient of their 2021 Founder’s Memorial, 2018 Distinguished Scholar, and 2011 Distinguished Service Award. He has been co-editor of the Journal of Advanced Academics and Gifted Child Quarterly. Dr. Siegle’s research interests include web0based instruction, motivation of gifted students, and teacher bias in the identification of students for gifted programs.