Novice programmers frequently adopt a syntax-specific and test-case-driven approach, writing code first and adjusting until programs compile and test cases pass, rather than developing correct solutions through systematic reasoning. AI coding tools exacerbate this challenge by providing syntactically correct but conceptually flawed solutions. In this paper, we address the question of developing correctness-first methodologies to enhance computational thinking in introductory programming education. To this end, we introduce BOOP (Blueprint, Operations, OCaml, Proof), a structured framework requiring four mandatory phases: formal specification, language-agnostic algorithm development, implementation, and correctness proof. This shifts focus from making the code work to understanding why the code is correct. BOOP was implemented at Ashoka University using a VS Code extension and preprocessor that enforces constraints. Initial evaluation shows improved algorithmic reasoning and reduced trial-and-error debugging. Students reported better edge case understanding and problem decomposition, though some initially found the format verbose. Instructors observed stronger foundational skills compared to traditional approaches, suggesting that structured correctness-first approaches may significantly improve students’ computational thinking abilities.