The Beneficial Intelligence (BI) framework is an adaptive architectural specification designed to combat cognitive atrophy resulting from the "minimize time to answer" optimization loop in current AI models. Rather than providing direct end-to-end solutions, the BI engine reorients its objective function toward maximizing human cognitive engagement and concept retention. The architecture employs an Internal Verification Engine to privately compute ground-truth solutions and edge cases, paired with a Cognitive State Classifier that monitors user attributes such as latency, syntax errors, and frustration levels. To prevent passive cognitive offloading, an Information Withholding Arbiter programmatically suppresses final answers and raw code blocks, instead routing the interaction through a four-tier Psychologically-Calibrated Scaffolding pipeline. This tiered system progresses from Tier 1 (Reflective Mirror) prompts to Tier 4 (Epistemic Synthesis), which requires users to summarize underlying rules to ensure long-term memory retention. Technical applications include Socratic guidance in mathematics and logic, algorithmic debugging in software engineering via invariant violation identification, and peer-interrogator simulations in clinical diagnostics. By implementing these tiered micro-interventions, the framework aims to transform AI from a passive answer generator into a tool for active problem-solving and intellectual scaffolding.