Aarav Dengla, who became India’s 93rd chess grandmaster in February 2026 at the age of 16, credits advances in artificial intelligence (AI) for accelerating his rise in the chess world. Dengla began playing competitive chess before he was six and, a decade later, used AI tools to strategize a key match in Bosnia that secured his grandmaster title.

Ahead of the match, Dengla analyzed his opponent’s recent games using two AI-powered chess engines that offered contrasting strategies: one recommended an aggressive, rapid attack, while the other suggested a slower, more methodical approach to wear down the opponent. He devised a plan to start aggressively and then shift to a conservative defense. However, during the game, unexpected losses of pieces forced him to choose between risking defeat by pressing on or settling for a draw. With time running out on his clock and no AI assistance available mid-game, he opted to continue pushing forward. Within ten moves, he cornered his opponent’s king, prompting resignation and earning the points needed for the title.

Dengla described his process of preparation as involving extensive use of large databases for scouting potential responses and employing two types of AI engines. One is a traditional, rules-based engine capable of calculating tens of thousands of move variations rapidly, while the other is a neural-network model inspired by Google DeepMind’s AlphaZero, which evaluates positions more holistically and proposes less obvious moves. Using both allows him to view the game from different strategic perspectives, although the final decision on moves remains his responsibility.

Reflecting on the broader implications of AI in education, Dengla challenged concerns that widespread AI use could degrade critical thinking or foster over-reliance. Drawing from his experience in chess, where players regularly train with AI tools but must rely on their own judgment during actual play, he argued that AI can raise skill levels rather than diminish them. He noted that the generation of chess players working with AI today is among the strongest in history, despite fears raised since Garry Kasparov's famous matches against IBM's Deep Blue in 1997 that AI would eclipse human competition.

Dengla suggested that educational institutions could adopt a similar framework by allowing AI support during study and preparation, while maintaining strict independence during assessments. He pointed out that large language models (LLMs) serve as patient, accessible tutors, enabling students to engage deeply with academic material and explore advanced concepts. For example, he credited AI language training apps with helping him achieve fluency in Spanish sufficient to take literature classes.

He acknowledged the need for adapting assessment methods, honor codes, and academic guardrails to accommodate AI’s growing presence. However, Dengla maintained that embracing AI tools holds the potential to accelerate knowledge advancement and intellectual development, just as these technologies have enhanced his chess training and performance. He urged against a closed mindset toward AI, emphasizing that the losses from resisting innovation would outweigh any risks associated with its integration into education and other fields.